ALBERT

Description: Worldwide bees provide an important ecosystem service of plant pollination. Climate change and land-use changes are among drivers threatening bee survival with mounting evidence of species decline and extinction. In developing countries, rural areas constitute a significant proportion of the country's land, but information is lacking on how different habitat types and weather patterns in these areas influence bee populations. This study investigated how weather variables and habitat-related factors influence the abundance, diversity, and distribution of bees across seasons in a farming rural area of Zimbabwe. Bees were systematically sampled in five habitat types (natural woodlots, pastures, homesteads, fields, and gardens) recording ground cover, grass height, flower abundance and types, tree abundance and recorded elevation, temperature, light intensity, wind speed, wind direction, and humidity. Zero-inflated models, censored regression models, and PCAs were used to understand the influence of explanatory variables on bee community composition, abundance, and diversity. Bee abundance was positively influenced by the number of plant species in flower (p 〈 .0001). Bee abundance increased with increasing temperatures up to 28.5°C, but beyond this, temperature was negatively associated with bee abundance. Increasing wind speeds marginally decreased probability of finding bees. Bee diversity was highest in fields, homesteads, and natural woodlots compared with other habitats, and the contributions of the genus Apis were disproportionately high across all habitats. The genus Megachile was mostly associated with homesteads, while Nomia was associated with grasslands. Synthesis and applications. Our study suggests that some bee species could become more proliferous in certain habitats, thus compromising diversity and consequently ecosystem services. These results highlight the importance of setting aside bee-friendly habitats that can be refuge sites for species susceptible to land-use changes.

Description: Background Although effects on labour is one of the most tangible and attributable climate impact, our quantification of these effects is insufficient and based on weak methodologies. Partly, this gap is due to the inability to resolve different impact channels, such as changes in time allocation (labour supply) and slowdown of work (labour productivity). Explicitly resolving those in a multi-model inter-comparison framework can help to improve estimates of the effects of climate change on labour effectiveness. Methods In this empirical, multi-model study, we used a large collection of micro-survey data aggregated to subnational regions across the world to estimate new, robust global and regional temperature and wet-bulb globe temperature exposure-response functions (ERFs) for labour supply. We then assessed the uncertainty in existing labour productivity response functions and derived an augmented mean function. Finally, we combined these two dimensions of labour into a single compound metric (effective labour effects). This combined measure allowed us to estimate the effect of future climate change on both the number of hours worked and on the productivity of workers during their working hours under 1·5°C, 2·0°C, and 3·0°C of global warming. We separately analysed low-exposure (indoors or outdoors in the shade) and high-exposure (outdoor in the sun) sectors. Findings We found differentiated empirical regional and sectoral ERF's for labour supply. Current climate conditions already negatively affect labour effectiveness, particularly in tropical countries. Future climate change will reduce global total labour in the low-exposure sectors by 18 percentage points (range −48·8 to 5·3) under a scenario of 3·0°C warming (24·8 percentage points in the high-exposure sectors). The reductions will be 25·9 percentage points (–48·8 to 2·7) in Africa, 18·6 percentage points (–33·6 to 5·3) in Asia, and 10·4 percentage points (–35·0 to 2·6) in the Americas in the low-exposure sectors. These regional effects are projected to be substantially higher for labour outdoors in full sunlight compared with indoors (or outdoors in the shade) with the average reductions in total labour projected to be 32·8 percentage points (–66·3 to 1·6) in Africa, 25·0 percentage points (–66·3 to 7·0) in Asia, and 16·7 percentage points (–45·5 to 4·4) in the Americas. Interpretation Both labour supply and productivity are projected to decrease under future climate change in most parts of the world, and particularly in tropical regions. Parts of sub-Saharan Africa, south Asia, and southeast Asia are at highest risk under future warming scenarios. The heterogeneous regional response functions suggest that it is necessary to move away from one-size-fits-all response functions to investigate the climate effect on labour. Our findings imply income and distributional consequences in terms of increased inequality and poverty, especially in low-income countries, where the labour effects are projected to be high.

Description: Code for coupling the Parallel Ice Sheet Model PISM with the Modular Ocean Model MOM

Description: The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). Version 2.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2019. Data sources of W5E5 are version 2.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2020), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003). Variables (with short names and units in brackets) included in the W5E5 dataset are Near Surface Relative Humidity (hurs, %), Near Surface Specific Humidity (huss, kg kg-1), Precipitation (pr, kg m-2 s-1), Snowfall Flux (prsn, kg m-2 s-1), Surface Air Pressure (ps, Pa), Sea Level Pressure (psl, Pa), Surface Downwelling Longwave Radiation (rlds, W m-2), Surface Downwelling Shortwave Radiation (rsds, W m-2), Near Surface Wind Speed (sfcWind, m s-1), Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and WFDE5-ERA5 Mask (mask, 1).

Description: Atlantic hurricane activity varies substantially from year to year and so do the associated damages. Longer-term forecasting of hurricane risks is a key element to reduce damages and societal vulnerabilities by enabling targeted disaster preparedness and risk reduction measures. While the immediate synoptic drivers of tropical cyclone formation and intensification are increasingly well understood, precursors of hurricane activity on longer time-horizons are still not well established. Here we use a causal network-based algorithm to identify physically motivated late-spring precursors of seasonal 15Atlantic hurricane activity. Based on these precursors we construct seasonal forecast models with competitive skill compared to operational forecasts. We present a skillful model to forecast July to October cyclone activity at the beginning of April.Earlier seasonal hurricane forecasting provides a multi-month lead time to implement more effective disaster risk reduction measures. Our approach also highlights the potential of applying causal effects network analysis in seasonal forecasting

Description: A portion of human-caused carbon dioxide emissions will stay in the atmosphere for hundreds of years, raising temperatures and sea levels globally. Most nations' emissions-reduction policies and actions do not seem to reflect this long-term threat, as collectively they point toward widespread permanent inundation of many developed areas. Using state-of-the-art new global elevation and population data, we show here that, under high emissions scenarios leading to 4 ∘C warming and a median projected 8.9 m of global mean sea level rise within a roughly 200- to 2000-year envelope, at least 50 major cities, mostly in Asia, would need to defend against globally unprecedented levels of exposure, if feasible, or face partial to near-total extant area losses. Nationally, China, India, Indonesia, and Vietnam, global leaders in recent coal plant construction, have the largest contemporary populations occupying land below projected high tide lines, alongside Bangladesh. We employ this population-based metric as a rough index for the potential exposure of the largely immovable built environment embodying cultures and economies as they exist today. Based on median sea level projections, at least one large nation on every continent but Australia and Antarctica would face exceptionally high exposure: land home to at least one-tenth and up to two-thirds of current population falling below tideline. Many small island nations are threatened with near-total loss. The high tide line could encroach above land occupied by as much as 15% of the current global population (about one billion people). By contrast, meeting the most ambitious goals of the Paris Climate Agreement will likely reduce exposure by roughly half and may avoid globally unprecedented defense requirements for any coastal megacity exceeding a contemporary population of 10 million.

Description: Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation; CV) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

Description: Tropical rainforests are recognized as one of the terrestrialtipping elements which could have profound impacts on the global cli-mate, once their vegetation has transitioned into savanna or grasslandstates. While several studies investigated the savannization of, e.g., theAmazon rainforest, few studies considered the influence of fire. Fire isexpected to potentially shift the savanna-forest boundary and henceimpact the dynamical equilibrium between these two possible vegeta-tion states under changing climate. To investigate the climate-inducedhysteresis in pan-tropical forests and the impact of fire under future cli-mate conditions, we employed the Earth system model CM2Mc, whichis biophysically coupled to the fire-enabled state-of-the-art dynamicglobal vegetation model LPJmL. We conducted several simulation ex-periments where atmospheric CO2concentrations increased (impactphase) and decreased from the new state (recovery phase), each withand without enabling wildfires. We find a hysteresis of the biomassand vegetation cover in tropical forest systems, with a strong regionalheterogeneity. After biomass loss along increasing atmospheric CO2concentrations and accompanied mean surface temperature increase ofabout 4°C (impact phase), the system does not recover completely intoits original state on its return path, even though atmospheric CO2concentrations return to their original state. While not detecting large-scale tipping points, our results show a climate-induced hysteresis intropical forest and lagged responses in forest recovery after the climatehas returned to its original state. Wildfires slightly widen the climate-induced hysteresis in tropical forests and lead to a lagged response inforest recovery by ca. 30 years.

Description: There is increasing evidence linking the mass-extinction event at the Cretaceous-Paleogene boundary to an asteroid impact near Chicxulub, Mexico. Here we use model simulations to explore the combined effect of sulfate aerosols, carbon dioxide and dust from the impact on the oceans and the marine biosphere in the immediate aftermath of the impact. We find a strong temperature decrease, a brief algal bloom caused by nutrients from both the deep ocean and the projectile, and moderate surface ocean acidification. Comparing the modeled longer-term post-impact warming and changes in carbon isotopes with empirical evidence points to a substantial release of carbon from the terrestrial biosphere. Overall, our results shed light on the decades to centuries after the Chicxulub impact which are difficult to resolve with proxy data.

Description: In verified generic programming, one cannot exploit the structure of concrete data types but has to rely on well chosen sets of specifications or abstract data types (ADTs). Functors and monads are at the core of many applications of functional programming. This raises the question of what useful ADTs for verified functors and monads could look like. The functorial map of many important monads preserves extensional equality. For instance, if f , g : A → B are extensionally equal, that is, ∀x ∈ A, f x = g x, then map f : List A → List B and map g are also extensionally equal. This suggests that preservation of extensional equality could be a useful principle in verified generic programming. We explore this possibility with a minimalist approach: we deal with (the lack of) extensional equality in Martin-Löf’s intensional type theories without extending the theories or using full-fledged setoids. Perhaps surprisingly, this minimal approach turns out to be extremely useful. It allows one to derive simple generic proofs of monadic laws but also verified, generic results in dynamical systems and control theory. In turn, these results avoid tedious code duplication and ad- hoc proofs. Thus, our work is a contribution towards pragmatic, verified generic programming.

Description: Output data used in the GMD publication: CM2Mc-LPJmL v1.0: Biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model, Drüke et al. (https://doi.org/10.5194/gmd-2020-436)

Description: Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution in response to different climate scenarios and assess the mass loss that would contribute to future sea level rise. However, there is currently no consensus on estimates of the future mass balance of the ice sheet, primarily because of differences in the representation of physical processes, forcings employed and initial states of ice sheet models. This study presents results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climate model results. Simulations of the Antarctic ice sheet contribution to sea level rise in response to increased warming during this period varies between −7.8 and 30.0 cm of sea level equivalent (SLE) under Representative Concentration Pathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment with constant climate conditions and should therefore be added to the mass loss contribution under climate conditions similar to present-day conditions over the same period. The simulated evolution of the West Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica mass change varies between −6.1 and 8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighing the increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelf collapse, here assumed to be caused by large amounts of liquid water ponding at the surface of ice shelves, yields an additional simulated mass loss of 28 mm compared to simulations without ice shelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, the calibration of these melt rates based on oceanic conditions taken outside of ice shelf cavities and the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario based on two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared to simulations done under present-day conditions for the two CMIP5 forcings used and display limited mass gain in East Antarctica.

Description: In coupled human-environment systems where well established and proven general theories are often lacking cluster analysis provides the possibility to discover regularities – a first step in empirically based theory building. The aim of this report is to share the experiences and knowledge on cluster analysis we gained in several applications in this realm helping to avoid typical problems and pitfalls. In our description of issues and methods we will highlight well-known main-stream methods as well as promising new developments, referring to pertinent literature for further information, thus offering also some potential new insights for the more experienced. The following aspects are discussed in detail: data-selection and pre-treatment, selection of a distance measure in the data space, selection of clustering method, performing clustering (parameterizing the algorithm(s), determining the number of clusters etc.) and the interpretation and evaluation of results. We link our description – as far as tools for performing the analysis are concerned - to the R software environment and its associated cluster analysis packages. We have used this public domain software, together with own tailor-made extensions, documented in the appendix.

Description: While the short-term economic impacts of extreme weather events are well documented, little is known about their impacts and transmission channels on economic growth in the long run. Using panel data regressions and national shares of people exposed to tropical cyclones and fluvial floods as exogenous predictors, we find output growth losses from severe tropical cyclones and fluvial floods to accumulate to −6.5% and −5.0% over 15 years, respectively. We further observe a strongly non-linear increase of these losses with disaster intensity. To understand how the observed impacts depend on the countries’ development level, we implement a country-specific regression framework. While we find evidence that higher development can prevent economic growth losses from fluvial floods, this is not the case for tropical cyclones. Further, we systematically study the economic and non-economic transmission channels through which these events impact on economic growth in the long run. We find that rising household consumption and government expenditure are the main growth-loss mitigating channels, whereas rising investment is the main growth-loss amplifying channel in the period 1971–2010.

Description: There exists a range of subsystems in the climate system exhibiting threshold behaviour which could be triggered under global warming within this century resulting in severe consequences for biosphere and human societies. While their individual tipping thresholds are fairly well understood, it is of yet unclear how their interactions might impact the overall stability of the Earth's climate system. This cannot be studied yet with state-of-the-art Earth system models due to computational constraints as well as missing and uncertain process representations of some tipping elements. Here, we explicitly study the effects of known physical interactions between the Greenland and West Antarctic Ice Sheet, the Atlantic Meridional Overturning Circulation, the El-Nino Southern Oscillation and the Amazon rainforest using a conceptual network approach. We analyse the risk of domino effects being triggered by each of the individual tipping elements under global warming in equilibrium experiments, propagating uncertainties in critical temperature thresholds and interaction strengths via a Monte-Carlo approach. Overall, we find that the interactions tend to destabilise the network. Furthermore, our analysis reveals the qualitative role of each of the five tipping elements showing that the polar ice sheets on Greenland and West Antarctica are oftentimes the initiators of tipping cascades, while the AMOC acts as a mediator, transmitting cascades. This implies that the ice sheets, which are already at risk of transgressing their temperature thresholds within the Paris range of 1.5 to 2 °C, are of particular importance for the stability of the climate system as a whole.

Description: Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

Description: We present a novel data set of subnational economic output, Gross Regional Product (GRP), for more than 1,500 regions in 77 countries that allows us to empirically estimate historic climate impacts at different time scales. Employing annual panel models, long-difference regressions and cross-sectional regressions, we identify effects on productivity levels and productivity growth. We do not find evidence for permanent growth rate impacts but we find robust evidence that temperature affects productivity levels considerably. An increase in global mean surface temperature by about 3.5C until the end of the century would reduce global output by 7-14% in 2100, with even higher damages in tropical and poor regions. Updating the DICE damage function with our estimates suggests that the social cost of carbon from temperature-induced productivity losses is on the order of 73-142$/tCO2 in 2020, rising to 92-181$/tCO2 in 2030. These numbers exclude non-market damages and damages from extreme weather events or sea-level rise.

Description: This article examines the effects of extreme weather events on internal migration in Mongolia. Our focus is on dzuds , extremely harsh winters characterized by very cold temperature, snowfall anomalies, and/or storms causing very high livestock mortality. We exploit exogenous variation in the intensity of extreme winter events across time and space to identify their causal impacts on permanent domestic migration. Our database is a time series of migration and population data at provincial and district level from official population registries, spanning the 1992-2018 period. Results obtained with a two-way fixed effects panel estimator show that extreme winter events cause significant and sizeable permanent out-migration from affected provinces for up to two years after an event. These effects are confirmed when considering net change rates in the overall population at the district level. The occurrence of extreme winter events is also a strong predictor for declines in the local population of pastoralist households, the socio-economic group most affected by those events. This suggests that the abandonment of pastoralist livelihoods is an important channel through which climate affects within-country migration.

Description: Future flood and drought risks have been predicted to transition from moderate to high levels at global warmings of 1.5 °C and 2.0 °C above pre-industrial levels, respectively. However, these results were obtained by approximating the equilibrium climate using transient simulations with steadily warming. This approach was recently criticised due to the warmer global land temperature and higher mean precipitation intensities of the transient climate in comparison with the equilibrium climate. Therefore, it is unclear whether floods and droughts projected under a transient climate can be systematically substituted for those occurring in an equilibrated climate. Here, by employing a large ensemble of global hydrological models (HMs) forced by global climate models, we assess the validity of estimating flood and drought characteristics under equilibrium climates from transient simulations. Differences in flood characteristics under transient and equilibrium climates could be largely ascribed to natural variability, indicating that the floods derived from a transient climate reasonably approximate the floods expected in an equally warm, equilibrated climate. By contrast, significant differences in drought intensity between transient and equilibrium climates were detected over a larger global land area than expected from natural variability. Despite the large differences among HMs in representing the low streamflow regime, we found that the drought intensities occurring under a transient climate may not validly represent the intensities in an equally warm equilibrated climate for approximately 6.7% of the global land area.

Description: Background: Women in rural Bangladesh face multiple, inter-related challenges including food insecurity, malnutrition, and low levels of empowerment. We aimed to investigate the pathway towards empowerment experienced by women participating in a three-year nutrition-sensitive homestead food production (HFP) program, which was evaluated through the Food and Agricultural Approaches to Reducing Malnutrition (FAARM) cluster-randomized controlled trial. Methods: We conducted 44 in-depth interviews and 12 focus group discussions with men and women in both intervention and control communities of the FAARM study site in rural, north-eastern Bangladesh. Using a modified grounded theory approach to data collection and analysis, we developed a framework to explain the pathway towards empowerment among HFP program participants. Results: The analysis and resulting framework identified seven steps towards empowerment: 1) receiving training and materials; 2) establishing home gardens and rearing poultry; 3) experiencing initial success with food production; 4) generating social or financial resources; 5) expanding agency in household decision-making; 6) producing renewable resources (e.g. farm produce) and social resources; and 7) sustaining empowerment. The most meaningful improvements in empowerment occurred among participants who were able to produce food beyond what was needed for household consumption and were able to successfully leverage these surplus resources to gain higher bargaining power in their household. Additionally, women used negotiation skills with their husbands, fostered social support networks with other women, and developed increased self-efficacy and motivation. Meanwhile, the least empowered participants lacked support in critical areas, such as support from their spouses, social support networks, or sufficient space or time to produce enough food to meaningfully increase their contribution and therefore bargaining power within their household. Conclusions: This study developed a novel framework to describe a pathway to empowerment among female participants in an HFP intervention, as implemented in the FAARM trial. These results have implications for the design of future nutrition-sensitive agriculture interventions, which should prioritize opportunities to increase empowerment and mitigate the barriers identified in our study. Trial registration: FAARM is registered with ClinicalTrials.gov (NCT02505711).

Description: In the past decades, human activities caused global Earth system changes, e.g., climate change or biodiversity loss. Simultaneously, these associated impacts have increased environmental awareness within societies across the globe, thereby leading to dynamical feedbacks between the social and natural Earth system. Contemporary modelling attempts of Earth system dynamics rarely incorporate such co-evolutions and interactions are mostly studied unidirectionally through direct or remembered past impacts. Acknowledging that societies have the additional capability for foresight, this work proposes a conceptual feedback model of socio-ecological co-evolution with the specific construct of anticipation acting as a mediator between the social and natural system. Our model reproduces results from previous sociological threshold models with bistability if one assumes a static environment. Once the environment changes in response to societal behaviour, the system instead converges towards a globally stable, but not necessarily desired, attractor. Ultimately, we show that anticipation of future ecological states then leads to metastability of the system where desired states can persist for a long time. We thereby demonstrate that foresight and anticipation form an important mechanism which, once its time horizon becomes large enough, fosters social tipping towards behaviour that can stabilise the environment and prevents potential socio-ecological collapse.

Description: The ongoing transition to renewable energy supply comes with a restructuring of power grids, changing their effective interaction topologies, more and more strongly decentralizing them and substantially modifying their input, output, and response characteristics. All of these changes imply that power grids become increasingly affected by collective, nonlinear dynamic phenomena, structurally and dynamically more distributed and less predictable in space and time, more heterogeneous in its building blocks, and as a consequence less centrally controllable. Here cornerstone aspects of data-driven and mathematical modeling of collective dynamical phenomena emerging in real and model power grid networks by combining theories from nonlinear dynamics, stochastic processes and statistical physics, anomalous statistics, optimization, and graph theory are reviewed. The mathematical background required for adequate modeling and analysis approaches is introduced, an overview of power system models is given, and a range of collective dynamical phenomena are focused on, including synchronization and phase locking, flow (re)routing, Braess’s paradox, geometric frustration, and spreading and localization of perturbations and cascading failures, as well as the nonequilibrium dynamics of power grids, where fluctuations play a pivotal role.

Description: Mass loss from the Antarctic Ice Sheet constitutes the largest uncertainty in projections of future sea level rise. Ocean-driven melting underneath the floating ice shelves and subsequent acceleration of the inland ice streams is the major reason for currently observed mass loss from Antarctica and is expected to become more important in the future. Here we show that for projections of future mass loss from the Antarctic Ice Sheet, it is essential (1) to better constrain the sensitivity of sub-shelf melt rates to ocean warming and (2) to include the historic trajectory of the ice sheet. In particular, we find that while the ice sheet response in simulations using the Parallel Ice Sheet Model is comparable to the median response of models in three Antarctic Ice Sheet Intercomparison projects – initMIP, LARMIP-2 and ISMIP6 – conducted with a range of ice sheet models, the projected 21st century sea level contribution differs significantly depending on these two factors. For the highest emission scenario RCP8.5, this leads to projected ice loss ranging from 1.4 to 4.0 cm of sea level equivalent in the ISMIP6 simulations where the sub-shelf melt sensitivity is comparably low, opposed to a likely range of 9.2 to 35.9 cm using the exact same initial setup, but emulated from the LARMIP-2 experiments with a higher melt sensitivity based on oceanographic studies. Furthermore, using two initial states, one with and one without a previous historic simulation from 1850 to 2014, we show that while differences between the ice sheet configurations in 2015 are marginal, the historic simulation increases the susceptibility of the ice sheet to ocean warming, thereby increasing mass loss from 2015 to 2100 by about 50 %. Our results emphasize that the uncertainty that arises from the forcing is of the same order of magnitude as the ice dynamic response for future sea level projections.

Description: This software can be used to quantify emissions mitigation targets stated in the Nationally Determined Contributions (NDCs). The output includes national targets and emissions pathways and globally aggregated mitigated emissions pathways. Several quantification options are available, including, i.a., the five marker scenarios of the Shared Socioeconomic Pathways (SSPs) as baseline trajectories.

Description: We develop a model of optimal carbon taxation and redistribution taking into account horizontal equity concerns by considering heterogeneous energy efficiencies. By deriving first- and second-best rules for policy instruments including carbon taxes, transfers and energy subsidies, we then investigate analytically how horizontal equity is considered in the social welfare maximizing tax structure. We calibrate the model to German household data and a 30 percent emission reduction goal. Our results show that energy-intensive households should receive more redistributive resources than energy-efficient households if and only if social inequality aversion is sufficiently high. We further find that redistribution of carbon tax revenue via household-specific transfers is the first-best policy. Equal per-capita transfers do not suffer from informational problems, but increase mitigation costs by around 15 percent compared to the first- best for unity inequality aversion. Adding renewable energy subsidies or non-linear energy subsidies, reduces mitigation costs further without relying on observability of households’ energy efficiency.

Description: Climate change affects societies across the globe in various ways. In addition to gradual changes in temperature and other climatic variables, global warming is likely to increase intensity and frequency of extreme weather events. Beyond biophysical impacts, these also directly affect societal and economic activity. Additionally, indirect effects can occur; spatially, economic losses can spread along global supply-chains; temporally, climate impacts can change the economic development trajectory of countries. This thesis first examines how climate change alters river flood risk and its local socio-economic implications. Then, it studies the global economic response to river floods in particular, and to climate change in general. Changes in high-end river flood risk are calculated for the next three decades on a global scale with high spatial resolution. In order to account for uncertainties, this assessment makes use of an ensemble of climate and hydrological models as well as a river routing model, that is found to perform well regarding peak river discharge. The results show an increase in high-end flood risk in many parts of the world, which require profound adaptation efforts. This pressure to adapt is measured as the enhancement in protection level necessary to stay at historical high-end risk. In developing countries as well as in industrialized regions, a high pressure to adapt is observed - the former to increase low protection levels, the latter to maintain the low risk levels perceived in the past. Further in this thesis, the global agent-based dynamic supply-chain model acclimate is developed. It models the cascading of indirect losses in the global supply network. As an anomaly model its agents - firms and consumers - maximize their profit locally to respond optimally to local perturbations. Incorporating quantities as well as prices on a daily basis, it is suitable to dynamically resolve the impacts of unanticipated climate extremes. The model is further complemented by a static measure, which captures the inter-dependencies between sectors across regions that are only connected indirectly. These higher-order dependencies are shown to be important for a comprehensive assessment of loss-propagation and overall costs of local disasters. In order to study the economic response to river floods, the acclimate model is driven by flood simulations. Within the next two decades, the increase in direct losses can only partially be compensated by market adjustments, and total losses are projected to increase by 17% without further adaptation efforts. The US and the EU are both shown to receive indirect losses from China, which is strongly affected directly. However, recent trends in the trade relations leave the EU in a better position to compensate for these losses. Finally, this thesis takes a broader perspective when determining the investment response to the climate change damages employing the integrated assessment model DICE. On an optimal economic development path, the increase in damages is anticipated as emissions and consequently temperatures increase. This leads to a significant devaluation of investment returns and the income losses from climate damages almost double. Overall, the results highlight the need to adapt to extreme weather events - local physical adaptation measures have to be combined with regional and global policy measures to prepare the global supply-chain network to climate change.

Description: Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2021-06-28.

Description: Tropical cyclones range among the costliest disasters on Earth. Their economic repercussions along the supply and trade network also affect remote economies that are not directly affected. We here simulate possible global repercussions on consumption for the example case of Hurricane Sandy in the US (2012) using the shock-propagation model Acclimate. The modeled shock yields a global three-phase ripple: an initial production demand reduction and associated consumption price decrease, followed by a supply shortage with increasing prices, and finally a recovery phase. Regions with strong trade relations to the US experience strong magnitudes of the ripple. A dominating demand reduction or supply shortage leads to overall consumption gains or losses of a region, respectively. While finding these repercussions in historic data is challenging due to strong volatility of economic interactions, numerical models like ours can help to identify them by approaching the problem from an exploratory angle, isolating the effect of interest. For this, our model simulates the economic interactions of over 7000 regional economic sectors, interlinked through about 1.8 million trade relations. Under global warming, the wave-like structures of the economic response to major hurricanes like the one simulated here are likely to intensify and potentially overlap with other weather extremes.

Description: Background: Anticipating changes in international migration patterns is useful for demographic studies and for designing policies that support the well-being of those involved. Existing forecasting methods do not account for a number of stylized facts that emerge from large-scale migration observations and theories: existing migrant communities – diasporas – act to lower migration costs and thereby provide a mechanism of self-amplification; return migration and transit migration are important components of global migration flows; and poverty constrains emigration. Objective: Here we present hindcasts and future projections of international migration that explicitly account for these nonlinear features. Methods: We develop a dynamic model that simulates migration flows by origin, destination, and place of birth. We calibrate the model using recently constructed global datasets of bilateral migration. Results: We show that the model reproduces past patterns and trends well based only on initial migrant stocks and changes in national incomes. We then project migration flows under future scenarios of global socioeconomic development. Conclusions: Different assumptions about income levels and between-country inequality lead to markedly different migration trajectories, with migration flows either converging towards net zero if incomes in presently poor countries catch up with the rest of the world; or remaining high or even rising throughout the 21st century if economic development is slower and more unequal. Importantly, diasporas induce significant inertia and sizable return migration flows. Contribution: Our simulation model provides a versatile tool for assessing the impacts of different socioeconomic futures on international migration, accounting for important nonlinearities in migration drivers and flows.

Description: Sustainable intensification (SI) of agriculture is a promising strategy for boosting the capacity of the agricultural sector to meet the growing demands for food and non-food products and services in a sustainable manner. Assessing and quantifying the options for SI remains a challenge due to its multiple dimensions and potential associated trade-offs. We contribute to overcoming this challenge by proposing an approach for the ex-ante evaluation of SI options and trade-offs to facilitate decision making in relation to SI. This approach is based on the utilization of a newly developed SI metrics framework (SIMeF) combined with agricultural systems modelling. We present SIMeF and its operationalization approach with modelling and evaluate the approach’s feasibility by assessing to what extent the SIMeF metrics can be quantified by representative agricultural systems models. SIMeF is based on the integration of academic and policy indicator frameworks, expert opinions, as well as the Sustainable Development Goals. Structured along seven SI domains and consisting of 37 themes, 142 sub-themes and 1128 metrics, it offers a holistic, generic, and policy-relevant dashboard for selecting the SI metrics to be quantified for the assessment of SI options in diverse contexts. The use of SIMeF with agricultural systems modelling allows the ex-ante assessment of SI options with respect to their productivity, resource use efficiency, environmental sustainability and, to a large extent, economic sustainability. However, we identify limitations to the use of modelling to represent several SI aspects related to social sustainability, certain ecological functions, the multi-functionality of agriculture, the management of losses and waste, and security and resilience. We suggest advancements in agricultural systems models and greater interdisciplinary and transdisciplinary integration to improve the ability to quantify SI metrics and to assess trade-offs across the various dimensions of SI.

Description: Power systems are subject to fundamental changes due to the increasing infeed of decentralized renewable energy sources and storage. The decentralized nature of the new actors in the system requires new concepts for structuring the power grid and achieving a wide range of control tasks ranging from seconds to days. Here, we introduce a multiplex dynamical network model covering all control timescales. Crucially, we combine a decentralized, self-organized low-level control and a smart grid layer of devices that can aggregate information from remote sources. The safety-critical task of frequency control is performed by the former and the economic objective of demand matching dispatch by the latter. Having both aspects present in the same model allows us to study the interaction between the layers. Remarkably, we find that adding communication in the form of aggregation does not improve the performance in the cases considered. Instead, the self-organized state of the system already contains the information required to learn the demand structure in the entire grid. The model introduced here is highly flexible and can accommodate a wide range of scenarios relevant to future power grids. We expect that it is especially useful in the context of low-energy microgrids with distributed generation. Highly decentralized power grids, possibly in the context of prosumer systems, require new concepts for their stable operation. We expect that both self-organized systems and intelligent devices with communication capability that can aggregate information from remote sources will play a central role. Here, we introduce a multiplex network model that combines both aspects and use it in a basic scenario and uncover surprising interactions between the layers.

Description: The call for a decent life for all within planetary limits poses a dual challenge: Provide all people with the essential resources needed to live well and, collectively, not exceed the source and sink capacity of the biosphere to sustain human societies. We examine the corridor of possible distributions of household energy and carbon footprints that satisfy both minimum energy use for a decent life and available energy supply compatible with the 1.5°C target in 2050. We estimated household energy and carbon footprints for expenditure deciles for 28 European countries in 2015 by combining data from national household budget surveys with the Environmentally-Extended Multi-Regional Input-Output model EXIOBASE. We found a top-to-bottom decile ratio (90:10) of 7.2 for expenditure, 3.1 for net energy and 2.6 for carbon. The lower inequality of energy and carbon footprints is largely attributable to inefficient energy and heating technologies in the lower deciles (mostly Eastern Europe). Adopting best technology across Europe would save 11 EJ of net energy annually, but increase environmental footprint inequality. With such inequality, both targets can only be met through the use of CCS, large efficiency improvements, and an extremely low minimum final energy use of 28 GJ per adult equivalent. Assuming a more realistic minimum energy use of about 55 GJ/ae and no CCS deployment, the 1.5°C target can only be achieved at near full equality. We conclude that achieving both stated goals is an immense and widely underestimated challenge, the successful management of which requires far greater room for maneuver in monetary and fiscal terms than is reflected in the current European political discourse.

Description: The COVID-19 pandemic reveals that societies place a high value on healthy lives. Leveraging this momentum to establish a more central role for human health in the policy process will provide further impetus to a sustainable transformation of energy and food systems.

Description: For light-duty vehicles (LDVs), alternative powertrains and liquid fuels based on renewable electricity are competing options considered by policymakers and stakeholders for achieving necessary CO2 emission reductions in the transport sector. While the urgency of climate change and the need to reach mitigation targets are well understood, system-wide implications along other sustainability dimensions need further exploration. We integrate a detailed transport system model into an integrated assessment framework and couple it with prospective life cycle impact analysis. This allows to assess different technological pathways of the European LDV fleet until 2050 for a comprehensive set of environmental and resource depletion indicators. Results indicate that greenhouse gas emissions drop significantly in all mitigation scenarios. However, impacts increase in several non-climate change impact categories even with fully renewable electricity supply. Additional impacts arise from the production of battery and fuel-cell components, and from a significant rise in electricity demand, most prominently for synthetic fuels. We consequently find that changes in mobility life-styles and in the relevant industrial processes are paramount to reduce environmental impacts from a climate-friendly LDV fleet across all categories.

Description: This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decision-makers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs and indicated user needs for the gridding and processing of model output.

Description: Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs a empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of “extremeness”. Intuitively, we think of this as calculating an anomalous p-value. This makes COPOD both parameter-free, highly interpretable, and computationally efficient. In this work, we make three key contributions, 1) propose a novel, parameterfree outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.

Description: In stochastic complex systems, some sudden critical transitions (CTs) from one desirable state to another contrasting one can take place because of noise, which may even lead to catastrophic consequences. To keep a certain system in one desirable state of performance, methods that suppress these catastrophic CTs in the presence of noise need to be developed. In this paper, the ability of an external linear augmentation method to suppress Gaussian white noise-induced CTs away from a desirable state is investigated from a new perspective. This control is designed in such a way that, as a noise-induced CT is impending, the desirable state of performance in a stochastic complex system can be stabilized using a specific type of coupling with a linear dynamical system. Then, the contrasting state is annihilated with increasing coupling strength. Taking a bi-stable system with one CT (from the desirable state to the undesirable one) and a tri-stable system with two CTs (from the desirable state to the sub-desirable one and from the sub-desirable state to the undesirable one) as the prototype class of real complex systems, the potential of our technique is demonstrated.

Description: Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.

Description: Input data for the PISM-MOM coupling framework as used in Kreuzer et al., Geoscientific Model Development publication (gmd-2020-230) [Coupling framework (1.0) for the ice sheet model PISM (1.1.1) and the ocean model MOM5 (5.1.0) via the ice-shelf cavity module PICO]

Description: More than 30 years ago, Diffusion-Limited Aggregation (DLA) has been studied as mechanism to generate structures sharing similarities with real-world cities. Recently, a stochastic gravitation model (SGM) has been proposed for the same purpose but representing a completely different mechanism. Both approaches have advantages and disadvantages, while e.g. the dendrites emerging via DLA are visually very different from real-world cities, the SGM does not preserve undeveloped locations close to the city center. Here we propose a unification of both mechanisms, i.e. a particle moves randomly and turns into an urban site with a probability that depends on the proximity to already developed sites. We study the cluster size distributions of the structures generated by both models and find that SGM generates more balanced distributions. We also propose a way to assess to which extent the largest cluster is a primate city and find that in both models, beyond certain parameter value, the size of the largest cluster becomes inconsistent with being drawn from the same distribution of remaining clusters.

Description: Parties to the Paris Agreement (PA, 2015) outline their planned contributions towards achieving the PA temperature goal to “hold […] the increase in the global average temperature to well below 2 ∘C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ∘C” (Article 2.1.a, PA) in their nationally determined contributions (NDCs). Most NDCs include targets to mitigate national greenhouse gas (GHG) emissions, which need quantifications to assess i.a. whether the current NDCs collectively put us on track to reach the PA temperature goals or the gap in ambition to do so. We implemented the new open-source tool “NDCmitiQ” to quantify GHG mitigation targets defined in the NDCs for all countries with quantifiable targets on a disaggregated level and to create corresponding national and global emissions pathways. In light of the 5-year update cycle of NDCs and the global stocktake, the quantification of NDCs is an ongoing task for which NDCmitiQ can be used, as calculations can easily be updated upon submission of new NDCs. In this paper, we describe the methodologies behind NDCmitiQ and quantification challenges we encountered by addressing a wide range of aspects, including target types and the input data from within NDCs; external time series of national emissions, population, and GDP; uniform approach vs. country specifics; share of national emissions covered by NDCs; how to deal with the Land Use, Land-Use Change and Forestry (LULUCF) component and the conditionality of pledges; and establishing pathways from single-year targets. For use in NDCmitiQ, we furthermore construct an emissions data set from the baseline emissions provided in the NDCs. Example use cases show how the tool can help to analyse targets on a national, regional, or global scale and to quantify uncertainties caused by a lack of clarity in the NDCs. Results confirm that the conditionality of targets and assumptions about economic growth dominate uncertainty in mitigated emissions on a global scale, which are estimated as 48.9–56.1 Gt CO2 eq. AR4 for 2030 (10th/90th percentiles, median: 51.8 Gt CO2 eq. AR4; excluding LULUCF and bunker fuels; submissions until 17 April 2020 and excluding the USA). We estimate that 77 % of global 2017 emissions were emitted from sectors and gases covered by these NDCs. Addressing all updated NDCs submitted by 31 December 2020 results in an estimated 45.6–54.1 Gt CO2 eq. AR4 (median: 49.6 Gt CO2 eq. AR4, now including the USA again) and increased coverage.

Description: There is evidence that a self-sustaining ice discharge from the West Antarctic Ice Sheet (WAIS) has started, potentially leading to its disintegration. The associated sea level rise of more than 3m would pose a serious challenge to highly populated areas including metropolises such as Calcutta, Shanghai, New York City, and Tokyo. Here, we show that the WAIS may be stabilized through mass deposition in coastal regions around Pine Island and Thwaites glaciers. In our numerical simulations, a minimum of 7400 Gt of additional snowfall stabilizes the flow if applied over a short period of 10 years onto the region (−2 mm year−1 sea level equivalent). Mass deposition at a lower rate increases the intervention time and the required total amount of snow. We find that the precise conditions of such an operation are crucial, and potential benefits need to be weighed against environmental hazards, future risks, and enormous technical challenges.

Description: The EU is currently preparing a major overhaul of its climate policy framework to deliver on the Green Deal’s new climate targets of a 55 percent cut in greenhouse gas (GHG) emis-sions relative to 1990 by 2030 and GHG neutrality by 2050. Ex-tending and strengthening the role of carbon pricing, imple-mented through the EU Emissions Trading System (EU-ETS), will play an important role in this framework. Accordingly, the design and governance of the EU-ETS will be ever more crucial. In this article, we focus both on the 2018 EU-ETS re-form as the first step on a slippery slope of increasing dis-cretionary intervention and on the upcoming reform risks reinforcing this trend. In their seminal work, Kydland and Prescott (1977) caution against such interventions, because of their ability to destabilize the market and engender recur-ring interventions. This limits the capacity of policymakers to credibly commit to long-term targets, which undermines the dynamic efficiency of intertemporal emissions trading sys-tems like the EU-ETS. To counteract this trend, we provide recommendations for rule-based adjustments to the EU-ETS.

Description: One of the most challenging issues in Mediterranean ecosystems to date has been to understand the emergence of discontinuous changes or catastrophic shifts. In the era of the 2030 Sustainable Development Goals, which encompass ideas around Land Degradation Neutrality, advancing this understanding has become even more critical and urgent. The aim of this paper is to synthesize insights into the drivers, processes and management of catastrophic shifts to highlight ways forward for the management of Mediterranean ecosystems. We use a multidisciplinary approach that extends beyond the typical single site, single scale, single approach studies in the current literature. We link applied and theoretical ecology at multiple scales with analyses and modeling of human–environment–climate relations and stakeholder engagement in six field sites in Mediterranean ecosystems to address three key questions: i) How do major degradation drivers affect ecosystem functioning and services in Mediterranean ecosystems? ii) What processes happen in the soil and vegetation during a catastrophic shift? iii) How can management of vulnerable ecosystems be optimized using these findings? Drawing together the findings from the use of different approaches allows us to address the whole pipeline of changes from drivers through to action. We highlight ways to assess ecosystem vulnerability that can help to prevent ecosystem shifts to undesirable states; identify cost-effective management measures that align with the vision and plans of land users; and evaluate the timing of these measures to enable optimization of their application before thresholds are reached. Such a multidisciplinary approach enables improved identification of early warning signals for discontinuous changes informing more timely and cost-effective management, allowing anticipation of, adaptation to, or even prevention of, undesirable catastrophic ecosystem shifts.

Description: Livestock is important for livelihoods of millions of people across the world and yet climate change risk and impacts assessments are predominantly on cropping systems. Climate change has significant impacts on Net Primary Production (NPP) which is a grassland dynamics indicator. This study aimed to analyze the spatio-temporal changes of NPP under climate scenario RCP2.6 and RCP8.5 in the grassland of Tanzania by 2050 and link this to potential for key livestock species. To this end, a regression model to estimate NPP was developed based on temperature (T), precipitation (P) and evapotranspiration (ET) during the period 2001–2019. NPP fluctuation maps under future scenarios were produced as difference maps of the current (2009–2019) and future (2050). The vulnerable areas whose NPP is mostly likely to get affected by climate change in 2050 were identified. The number of livestock units in grasslands was estimated according to NPP in grasslands of Tanzania at the Provincial levels. The results indicate the mean temperature and evapotranspiration are projected to increase under both emission scenarios while precipitation will decrease. NPP is significantly positively correlated with Tmax and ET and projected increases in these variables will be beneficial to NPP under climate change. Increases of 17% in 2050 under RCP8.5 scenario are projected, with the southern parts of the country projected to have the largest increase in NPP. The southwest areas showed a decreasing trend in mean NPP of 27.95% (RCP2.6) and 13.43% (RCP8.5). The highest decrease would occur in the RCP2.6 scenario in Ruvuma Province, by contrast, the mean NPP value in the western, eastern, and central parts would increase in 2050 under both Scenarios, the largest increase would observe in Kilimanjaro, Dar-Es-Salaam and Dodoma Provinces. It was found that the number of grazing livestock such as cattle, sheep, and goats will increase in the Tanzania grasslands under both climate scenarios. As the grassland ecosystems under intensive exploitation are fragile ecosystems, a combination of improving grassland productivity and grassland conservation under environmental pressures such as climate change should be considered for sustainable grassland management.

Description: Ambient temperature has been identified as a potential cause for human conflict in a variety of studies. Conflict is no longer limited to the physical space but exists in the form of hate and discrimination on social media. Here we provide evidence that the amount of racist and xenophobic content posted to the social media platform Twitter is nonlinearly influenced by temperature. Exploiting the linguistic plurality of Europe, we statistically analyse daily temperature data and more than 10 million racist tweets from six different countries spanning several climate zones for the years 2012-2018. Using a fixed-effects panel regression model that utilizes exogenous variation in local weather and controls for unobserved omitted variables, we identify the effect of population-weighted daily average temperature on the daily number of racist tweets and likes. We find a quasi-quadratic temperature response of racist tweets that is inversely proportional to the temperature distribution. Fewest racist tweets and likes are found for daily average temperatures between 5°C and 11°C, i.e. temperatures that are frequently experienced. Temperatures warmer or colder than that are associated with steep, nonlinear increases. Analyses at the country-level confirm this climate comfort zone of 5°C to 11°C across different European climatic zones. In the Southern European countries this is colder than the most frequently experienced temperatures, pointing to possible limits of adaptation. Within the next 30 years, the number of days outside this climate comfort zone, weighted by the identified temperature-racist-tweet response curve, will increase across parts of Europe, indicating that rising temperatures could aggravate xenophobia and racism in social media.

Description: COVID-19 has revealed how challenging it is to manage global, systemic and compounding crises. Like COVID-19, climate change impacts, and maladaptive responses to them, have potential to disrupt societies at multiple scales via networks of trade, finance, mobility and communication, and to impact hardest on the most vulnerable. However, these complex systems can also facilitate resilience if managed effectively. This review aims to distil lessons related to the transboundary management of systemic risks from the COVID-19 experience, to inform climate change policy and resilience building. Evidence from diverse fields is synthesised to illustrate the nature of systemic risks and our evolving understanding of resilience. We describe research methods that aim to capture systemic complexity to inform better management practices and increase resilience to crises. Finally, we recommend specific, practical actions for improving transboundary climate risk management and resilience building. These include mapping the direct, cross-border and cross-sectoral impacts of potential climate extremes, adopting adaptive risk management strategies that embrace heterogenous decision-making and uncertainty, and taking a broader approach to resilience which elevates human wellbeing, including societal and ecological resilience.

Description: This paper designs an event-triggering based communication strategy for the global attitude synchronization of a network of rigid bodies. To overcome the topological constraint on the manifold SO(3), the quaternion-based hybrid control strategy is designed using a binary logic variable, relying on the relative measurements of adjacent rigid bodies, to determine the torque orientation. The Zeno-free distributed event-triggering strategies (ETSs) are designed combining with the reset of the binary logic variable to generate discrete communication instants, where only the corresponding parts of the control inputs are updated at those discrete instants. By assuming perfect knowledge of the rigid bodies' dynamics and considering uncertainties and/or exogenous disturbances simultaneously, nominal and robust cases are analyzed to ensure the global attitude synchronization, respectively. The effectiveness of the main results is demonstrated by considering the attitude synchronization of six miniature quadrotor prototypes.

Description: Although spatial polarization of attitudes is extremely common around the world, we understand little about the mechanisms through which polarization on divisive issues rises and falls over time. We develop a theory that explains how political shocks can have different effects in different regions of a country depending upon local dynamics generated by the preexisting spatial distribution of attitudes and discussion networks. Where opinions were previously divided, attitudinal diversity is likely to persist after the shock. Meanwhile, where a clear pre-crisis majority exists on key issues, opinions should change in the direction of the predominant view. These dynamics result in greater local homogeneity in attitudes but at the same time exacerbate geographic polarization across regions and sometimes even within regions. We illustrate our theory by developing a modified version of the adaptive voter model (AVM), an adaptive network model of opinion dynamics, to study changes in attitudes toward the EU in Ukraine in the context of the Euromaidan Revolution of 2013-14. Using individual-level panel data from surveys fielded before and after the Euromaidan Revolution, we show that EU support increased in areas with high prior public support for EU integration but declined further where initial public attitudes were opposed to the EU, thereby increasing the spatial polarization of EU attitudes in Ukraine. Our tests suggest that the predictive power of both network and regression models increases significantly when we incorporate information about the geographic location of network participants, which highlights the importance of spatially rooted social networks.

Description: Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.

Description: The COVID-19 pandemic has disrupted global human mobility dynamics. This IMR Dispatch examines the historical, bidirectional links between pandemics and mobility and provides an early analysis of how they unfolded during the first nine months of the COVID-19 emergency. Results show, first, that international travel restrictions to combat the spread of the coronavirus are not a panacea in and of themselves. Second, our analysis demonstrates that the pandemic, government responses, and resulting economic impacts can lead to the involuntary immobility of at-risk populations, such as aspiring asylum-seekers or survival migrants. In a similar fashion, stay-at-home measures have posed dire challenges for those workers who lack options to work from home, as well as for migrants living in precarious, crowded circumstances. Moreover, global economic contraction has increased involuntary immobility by reducing both people’s resources to move and the demand for labor. Third, we show that people’s attempts to protect themselves from the virus can result in shifting patterns of mobility, such as increases in cross-border return migration and urban-to-rural movements. Drawing on international guidance for measures to combat pandemics and relevant frameworks on mobility, we propose approaches to alleviate the burden of travel restrictions on migrants and people aspiring to move, while still addressing the need to contain the pandemic and lessen its repercussions.

Description: This collection provides a contemporary excerpt of “Cities as complex systems”. The contributions have been submitted between April and October 2020. We briefly discuss example papers addressing the themes “urban scaling”, “urban mobility”, “flows in cities”, “spatial analysis”, “information technology and cities”, and “cities in time”. After motivating the intersection of cities and complexity, we provide an introduction and additional thoughts on urban scaling.

Description: Coastal cities are exposed to high risks due to climate change, as they are potentially affected by both rising sea levels and increasingly intense and frequent coastal storms. Socio-economic drivers also increase exposure to natural hazards, accelerate environmental degradation, and require adaptive governance structures to moderate negative impacts. Here, we use a social network analysis (SNA) combined with further qualitative information to identify barriers and enablers of adaptive governance in the Barcelona metropolitan area. By analyzing how climate change adaptation is mainstreamed between different administrative scales as well as different societal actors, we can determine the governance structures and external conditions that hamper or foster strategical adaptation plans from being used as operational adaptation tools. We identify a diverse set of stakeholders acting at different administrative levels (local to national), in public administration, science, civil society, and the tourism economy. The metropolitan administration acts as an important bridging organization by promoting climate change adaptation to different interest groups and by passing knowledge between actors. Nonetheless, national adaptation planning fails to take into account local experiences in coastal protection, which leads to an ineffective science policy interaction and limits adaptive management and learning opportunities. Overcoming this is difficult, however, as the effectiveness of local adaptation strategies in the Barcelona metropolitan area is very limited due to a strong centralization of power at the national level and a lack of polycentricity. Due to the high touristic pressure, the legal framework is currently oriented to primarily meet the demands of recreational use and tourism, prioritizing these aspects in daily management practice. Therefore, touristic and economic activities need to be aligned to adaptation efforts, to convert them from barriers into drivers for adaptation action. Our work strongly suggests that more effectively embedding adaptation planning and action into existing legal structures of coastal management would allow strategic adaptation plans to be an effective operational tool for local coastal governance.

Description: We develop a model of optimal carbon taxation and redistribution taking into account horizontal equity concerns by considering heterogeneous energy efficiencies. By deriving first- and second-best rules for policy instruments including carbon taxes, transfers and energy subsidies, we then investigate analytically how horizontal equity is considered in the social welfare maximizing tax structure. We calibrate the model to German household data and a 30 percent emission reduction goal. Our results show that energy-intensive households should receive more redistributive resources than energy-efficient households if and only if social inequality aversion is sufficiently high. We further find that redistribution of carbon tax revenue via household-specific transfers is the first-best policy. Equal per-capita transfers do not suffer from informational problems, but increase mitigation costs by around 15 percent compared to the first-best for unity inequality aversion. Adding renewable energy subsidies or non-linear energy subsidies, reduces mitigation costs further without relying on observability of households’ energy efficiency.

Description: People across Peru are vulnerable and exposed to a wide range of hazards, and studies demonstrate that these hazards are key drivers of migration in the country. Hydrometeorological hazards resulting in excessive amounts of water (in such forms as torrential rainfalls and floods) – or the lack thereof (in the form of, for example, drought or glacier retreat) – are particularly salient to migration. Climate change has intensified these hazards and will continue to do so, possibly resulting in new and unparalleled impacts on migration. IOM and the Potsdam Institute for Climate Impact Research have partnered to produce this report, which seeks to shed light on the available evidence on the environment, climate change and migration nexus in Peru. The study puts into perspective various climate risks and hazards that affect communities in the country’s main topographical zones: the coast, the highlands, and the rainforest or jungle. The report provides a systematic review of the complex interaction between climate and other factors driving migration in the country. It discusses the necessity to understand climate migration patterns and improve planning and policies in the short term to the mid-term, in view of several “no-analog threats” – that is, those with unprecedented, large impacts – that could occur towards the end of the century.

Description: With the prevalence of COVID-19, the modeling of epidemic propagation and its analyses have played a significant role in controlling epidemics. However, individual behaviors, in particular the self-protection and migration, which have a strong influence on epidemic propagation, were always neglected in previous studies. In this paper, we mainly propose two models from the individual and population perspectives. In the first individual model, we introduce the individual protection degree that effectively suppresses the epidemic level as a stochastic variable to the SIRS model. In the alternative population model, an open Markov queueing network is constructed to investigate the individual number of each epidemic state, and we present an evolving population network via the migration of people. Besides, stochastic methods are applied to analyze both models. In various simulations, the infected probability, the number of individuals in each state and its limited distribution are demonstrated.

Description: This archive provides the model result of the AbuMIP experiments: The Antarctic BUttressing Model Intercomparison Project.

Description: Sequestration of soil organic carbon (SOC) on cropland has been proposed as a climate change mitigation strategy to reduce global greenhouse gas (GHG) concentrations in the atmosphere, which is in particular needed to achieve the targets proposed in the Paris Agreement to limit the increase in atmospheric temperature to well below 2 °C. We here analyze the historical evolution and future development of cropland SOC using the global process-based biophysical model LPJmL, which was recently extended by a detailed representation of tillage practices and residues management (version 5.0–tillage2). We find that model results for historical global estimates for SOC stocks are at the upper end of available literature, with ~2650 Pg C of SOC stored globally in the year 2018, of which ~170 Pg C are stored in cropland soils. In future projections, assuming no further changes in current cropland patterns and under four different management assumptions with two different climate forcings, RCP2.6, and RCP8.5, results suggest that agricultural SOC stocks decline in all scenarios, as the decomposition of SOC outweighs the increase of carbon inputs into the soil from altered management practices. Different climate-change scenarios, as well as assumptions on tillage management, play a minor role in explaining differences in SOC stocks. The choice of tillage practice explains between 0.2 % and 1.3 % of total cropland SOC stock change in the year 2100. Future dynamics in cropland SOC are most strongly controlled by residue management, whether residues are left on the field or harvested. We find that on current cropland, global cropland SOC stocks decline until the end of the century by only 1.0 % to 1.4 % if residue-retention management systems are generally applied and by 26.7 % to 27.3 % in case of residues harvest. For different climatic regions, increases in cropland SOC can only be found for tropical dry, warm temperate moist, and warm temperate dry regions in management systems that retain residues.

Description: The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5–95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20–28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s.

Description: The epidemic threshold of a social system is the ratio of infection and recovery rate above which a disease spreading in it becomes an epidemic. In the absence of pharmaceutical interventions (i.e. vaccines), the only way to control a given disease is to move this threshold by non-pharmaceutical interventions like social distancing, past the epidemic threshold corresponding to the disease, thereby tipping the system from epidemic into a non-epidemic regime. Modeling the disease as a spreading process on a social graph, social distancing can be modeled by removing some of the graphs links. It has been conjectured that the largest eigenvalue of the adjacency matrix of the resulting graph corresponds to the systems epidemic threshold. Here we use a Markov chain Monte Carlo (MCMC) method to study those link removals that do well at reducing the largest eigenvalue of the adjacency matrix. The MCMC method generates samples from the relative canonical network ensemble with a defined expectation value of λ max λmax . We call this the “well-controlling network ensemble” (WCNE) and compare its structure to randomly thinned networks with the same link density. We observe that networks in the WCNE tend to be more homogeneous in the degree distribution and use this insight to define two ad-hoc removal strategies, which also substantially reduce the largest eigenvalue. A targeted removal of 80% of links can be as effective as a random removal of 90%, leaving individuals with twice as many contacts. Finally, by simulating epidemic spreading via either an SIS or an SIR model on network ensembles created with different link removal strategies (random, WCNE, or degree-homogenizing), we show that tipping from an epidemic to a non-epidemic state happens at a larger critical ratio between infection rate and recovery rate for WCNE and degree-homogenized networks than for those obtained by random removals.

Description: The EMF-30 study uses a scenario approach in which a coordinated suite of scenarios is used to explore how stylized global policies to reduce SLCFs impacts emissions, concentrations, radiative forcing, and global temperature change. The SLCF reduction scenarios focus on methane across all sectors and black carbon from transportation and buildings. Greenhouse gas (GHG) mitigation scenarios are also considered, including the combination of GHG and SLCF mitigation.

Description: In control theory, to solve a finite-horizon sequential decision problem (SDP) commonly means to find a list of decision rules that result in an optimal expected total reward (or cost) when taking a given number of decision steps. SDPs are routinely solved using Bellman’s backward induction. Textbook authors (e.g. Bertsekas or Puterman) typically give more or less formal proofs to show that the backward induction algorithm is correct as solution method for deterministic and stochastic SDPs. Botta, Jansson and Ionescu propose a generic framework for finite horizon, monadic SDPs together with a monadic version of backward induction for solving such SDPs. In monadic SDPs, the monad captures a generic notion of uncertainty, while a generic measure function aggregates rewards. In the present paper we define a notion of correctness for monadic SDPs and identify three conditions that allow us to prove a correctness result for monadic backward induction that is comparable to textbook correctness proofs for ordinary backward induction. The conditions that we impose are fairly general and can be cast in category-theoretical terms using the notion of Eilenberg-Moore-algebra. They hold in familiar settings like those of deterministic or stochastic SDPs but we also give examples in which they fail. Our results show that backward induction can safely be employed for a broader class of SDPs than usually treated in textbooks. However, they also rule out certain instances that were considered admissible in the context of Botta et al. ’s generic framework. Our development is formalised in Idris as an extension of the Botta et al. framework and the sources are available as supplementary material.

Description: Pathways towards limiting global warming to well below 2°C, as used by the IPCC in the Fifth Assessment Report, do not consider the climate impacts already occurring below 2°C. Here we show that accounting for such damages significantly increases the near-term ambition of transformation pathways. We use econometric estimates of climate damages on GDP growth, and explicitly model the uncertainty in the persistence time of damages. The Integrated Assessment Model we use includes the climate system and mitigation technology detail required to derive near-term policies. We find an optimal carbon price of $115 per tonne of CO2 in 2030. The long-term persistence of damages, while highly uncertain, is a main driver of the near-term carbon price. Accounting for damages on economic growth increases the gap between the currently pledged nationally determined contributions and the welfare-optimal 2030 emissions by two thirds, compared to pathways considering the 2°C limit only.

Description: Antarctica’s ice shelves modulate the grounded ice flow, and weakening of ice shelves due to climate forcing will decrease their ‘buttressing’ effect, causing a response in the grounded ice. While the processes governing ice-shelf weakening are complex, uncertainties in the response of the grounded ice sheet are also difficult to assess. The Antarctic BUttressing Model Intercomparison Project (ABUMIP) compares ice-sheet model responses to decrease in buttres-sing by investigating the ‘end-member’ scenario of total and sustained loss of ice shelves. Although unrealistic, this scenario enables gauging the sensitivity of an ensemble of 15 ice-sheet models to a total loss of buttressing, hence exhibiting the full potential of marine ice-sheet instability. All models predict that this scenario leads to multi-metre (1–12 m) sea-level rise over 500 years from present day. West Antarctic ice sheet collapse alone leads to a 1.91–5.08 m sea-level rise due to the marine ice-sheet instability. Mass loss rates are a strong func-tion of the sliding/friction law, with plastic laws cause a further destabilization of the Aurora and Wilkes Subglacial Basins, East Antarctica. Improvements to marine ice-sheet models have greatly reduced variability between modelled ice-sheet responses to extreme ice-shelf loss, e.g. compared to the SeaRISE assessments.

Description: Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is 〉 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge 〉 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds.

Description: The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

Description: The impacts of climate change are projected to become more intense and frequent. One of the indirect impacts of climate change is food insecurity. Agriculture in Pakistan, measured fourth best in the world, is already experiencing visible adverse impacts of climate change. Among many other food sources, potato crop remains one of the food security crops for developing nations. Potatoes are widely cultivated in Pakistan. To assess the impact of climate change on potato crop in Pakistan, it is imperative to analyze its distribution under future climate change scenarios using Species Distribution Models (SDMs). Maximum Entropy Model is used in this study to predict the spatial distribution of Potato in 2070 using two CMIP5 models for two climate change scenarios (RCP 4.5 and RCP 8.5). 19 Bioclimatic variables are incorporated along with other contributing variables like soil type, elevation and irrigation. The results indicate slight decrease in the suitable area for potato growth in RCP 4.5 and drastic decrease in suitable area in RCP 8.5 for both models. The performance evaluation of the model is based on AUC. AUC value of 0.85 suggests the fitness of the model and thus, it is applicable to predict the suitable climate for potato production in Pakistan. Sustainable potato cultivation is needed to increase productivity in developing countries while promoting better resource management and optimization.

Description: The Atlantic Meridional Overturning Circulation (AMOC) is likely the most well-known system of ocean currents on Earth, redistributing heat, nutrients and carbon over a large part of the Earth’s surface and affecting global climate as a result. Due to enhanced freshwater fluxes into the subpolar North Atlantic as a response to global warming, the AMOC is expected, and may have already started, to weaken and these changes will likely have global impacts. It is therefore of considerable relevance to improve our understanding of past and future AMOC changes. My thesis tries to answer some of the open questions in this field by giving strong evidence that the AMOC has already weakened over the last century, by narrowing future projections of this slowdown and by studying the impacts on global surface warming.

Description: To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). The SWAT+ model represents agricultural land by default in a generic way where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for Northeast Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural Hydrologic Response Units (HRUs) which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in regional to global hydrological models.

Description: The planetary boundaries concept was formulated in 2009 by an international research group led by Johan Rockström,and revised in 2015 by Will Steffen and colleagues(Rockström et al. 2009; Steffen et al. 2015b). The concept emerged against the background of the Anthropocene–a period of timeasso-ciated with rapidly growing environmental pressures,and increasing degradation and scarcity of global environmental resources,which intensifies risks for the stability and functioning of the Earth system. The human burden on the global environment and ecosystems has reached a level where sud-den non-linear systemic changes can no longer be ruled out. The UFOPLAN project 'Planetary Boundaries –Challengesfor Science, Civil Society and Politics' (FKZ 3714 100 0) addresses the challenge of operationalizing the planetary boundaries concept. The project analyzesthe requirements that the concept places on politics, science, civil society and business, with the aim ofinformingthe political implementation of the concept. In addition to cross-sectional papers that deal with topics of significance for various political processes (e.g., the potential for environmen-tal communication, risks associated withtransgressing boundaries and a methodology for operational-ization), political focus topics were addressed. The political focus topics addressquestionsof how the planetary boundaries conceptcan be applied to specific environmental policymakingareas (e.g.,the formulation of a national nitrogen strategy and the Integrated Environmental Programme 2030). Important next steps for the operationalization of the concept include thestronger inclusion of plane-tary boundaries in sustainability strategies and a reorientation of science fundingtoward the inte-grated development of the concept.

Description: Cereal crop production in sub-Saharan Africa has not achieved the much-needed increase in yields to foster economic development and food security. Maize yields in the region’s semi-arid agroecosystems are constrained by highly variable rainfall, which may be worsened by climate change. Thus, the Tanzanian government has prioritised agriculture as an adaptation sector in its Intended Nationally Determined Contribution, and crop management adjustments as a key investment area in its Agricultural Sector Development Programme. In this study, we investigated how future changes in maize yields under different climate scenarios can be countered by regional adjusted crop management and cultivar adaptation strategies. A crop model was used to simulate maize yields in the Singida Region of Tanzania for the baseline period 1980-2012 and under three future climate projections for 2020- 2060 and 2061-2099. Adaptation strategies to improve yields were full irrigation, deficit irrigation, mulch and nitrogen addition and another cultivar. According to our model results, increase in temperature is the main driver of future maize yield decline. Increased respiration and phenological development were associated with lower maize yields of 16% in 2020-2060 and 20% in 2061-2099 compared to the 1980-2012 baseline. Surprisingly, none of the management strategies significantly improved yields; however, a different maize variety that was tested as an alternative coping strategy performed better. This study suggests that investment in accessibility of improved varieties and investigation of maize traits that have the potential to perform well in a warmer future are better suited for sustaining maize production in the semi-arid region than adjustments in crop management.

Description: In their Comment on our paper (Caesar et al., 2019), Chen and Tung (hereafter C&T) argue that our analysis, showing that over the last decades AMOC strength and global mean surface temperature were positively correlated, is incorrect. Their claim is mainly based on two arguments, neither of which is justified: First, C&T claim that our analysis is based on "established evidence" that was only true for preindustrial conditions – this is not the case. Using data from the modern period (1947-2012), we show that the established understanding (i.e. deep-water formation in the North Atlantic cools the deep ocean and warms the surface) is correct, but our analysis is not based on this fact. Secondly, C&T claim that our results are based on a statistical analysis of only one cycle of data which was furthermore incorrectly detrended. This, too, is not true. Our conclusion that a weaker AMOC delays the current surface warming rather than enhances it, is based on several independent lines of evidence. The data we show to support this covers more than one cycle and the detrending (which was performed to avoid spurious correlations due to a common trend) does not affect our conclusion: the correlation between AMOC strength and global mean surface temperature is positive. We do not claim that this is strong evidence that the two time series are in phase, but rather that this means that the two time series are not anti-correlated.

Description: Abstract. Tropical convective activity represents a source of predictability for mid-latitude weather in the Northern Hemi-sphere. In winter, the El Niño–Southern Oscillation (ENSO) is the dominant source of predictability in the tropics and ex-tratropics, but its role in summer is much less pronounced and the exact teleconnection pathways are not well under-stood. Here, we assess how tropical convection interacts with mid-latitude summer circulation at different intra-seasonal timescales and how ENSO affects these interactions. First, we apply maximum covariance analysis (MCA) between tropical convective activity and mid-latitude geopotential height fields to identify the dominant modes of interaction. The first MCA mode connects the South Asian monsoon with the mid-latitude circumglobal teleconnection pattern. The second MCA mode connects the western North Pacific summer monsoon in the tropics with a wave-5 pattern centred over the North Pacific High in the mid-latitudes. We show that the MCA patterns are fairly insensitive to the selected intra-seasonal timescale from weekly to 4-weekly data. To study the potential causal interdependencies between these modes and with other atmospheric fields, we apply the causal discovery method PCMCI at different timescales. PCMCI extends standard correlation analysis by removing the con-founding effects of autocorrelation, indirect links and com-mon drivers. In general, there is a two-way causal interaction between the tropics and mid-latitudes, but the strength and sometimes sign of the causal link are timescale dependent. We introduce causal maps that show the regionally specific causal effect from each MCA mode. Those maps confirm the dominant patterns of interaction and in addition high-light specific mid-latitude regions that are most strongly con-nected to tropical convection. In general, the identified causal teleconnection patterns are only mildly affected by ENSO and the tropical mid-latitude linkages remain similar. Still, La Niña strengthens the South Asian monsoon generating a stronger response in the mid-latitudes, while during El Niño years the Pacific pattern is reinforced. This study paves the way for process-based validation of boreal summer telecon-nections in (sub-)seasonal forecast models and climate mod-els and therefore works towards improved sub-seasonal pre-dictions and climate projections.

Description: A methodology to assess future development in patterns of vulnerability is presented which can support the assessment of global policies with regard to their impacts on specific vulnerabilities on the regional or local scale. Patterns of vulnerability, formalized by vulnerability profiles (e.g. for the livelihoods of dryland smallholder farmers) were investigated under different consistent indicator scenarios reflecting different global policies. After unfolding several principal possibilities to do such an analysis of temporal change in vulnerability patterns we could conclude that the concept of “Clusters of Change” (CoCs) is the most straight forward and promising approach. The main arguments are that each interpretation has necessarily to consider both, the starting situation and it’s change over time (”poor and heavily improving”, ”rich and stagnating” etc.). This implies that we are looking for patterns which represent typical combinations of present states AND expected future changes. An application of the CoC-concept to the drylands vulnerability patterns considering the indicator set for the present situation and the same indicator set for 2050 under a baseline scenario was performed as a test. Comparison of the present vulnerability cluster partition with the spatial distribution of the CoCs revealed that most of these clusters are separated into an improving and a deteriorating part which shows where winners and losers of the baseline scenario are – an interesting result which illustrates the appropriateness of the CoC – method. To explore the potential of CoCs for the dryland vulnerability we applied the method to two different sets of scenarios until 2050: a baseline vs. Climate policy scenario (OECD, 2012) and a ”policy first” scenario vs. ”security first” scenario (UNEP, 2007). The first one serves as an example for a policy assessment while the second compares the vulnerability consequences of two scenarios based on different story-lines of further global development. The main conclusion to be drawn from these calculations is that the CoCs are rather insensitive with regard to the small differences between the scenarios. Regarding the first set of scenarios the relatively short time horizon of relevant influences of climate policies on climate change impacts and several indicators which are not influenced at all generate only a very small difference. The only significant change in the resulting vulnerability profiles was in the values of change in water scarcity: it was lower for all profiles in the climate policy case. The second set of scenarios is not directly related to policy decisions but to different global story-lines which deviate stronger. This resulted in an increasing cluster number from 4 (policy first) to 5 (security first) clusters, about 20% of the pixels changing cluster membership, 3 clusters showing the same spatial extent for both scenarios but the 4th cluster (“policy first”) “losing” India which generates a separate cluster in the “security first” scenario. This allows for the interpretation that a further development according to the “security first”-storyline compared to the “policy first”-storyline would make a difference particularly for India. Closer inspection of the respective profile shows a qualitatively different situation indicating increased vulnerability compared to the “policy first” scenario where India shares one cluster with e.g., Northern Africa.

Description: Pine Island Glacier in West Antarctica is among the fastest changing glaciers worldwide. Over the last two decades, the glacier has lost in excess of a trillion tons of ice, or the equivalent of 3 mm of sea level rise. The ongoing changes are commonly attributed to ocean-induced thinning of its floating ice shelf and the associated reduction in buttressing forces. However, other drivers of change such as large-scale calving, changes in ice rheology and basal slipperiness could play a vital, yet unquantified, role in controlling the ongoing and future evolution of the glacier. In addition, recent studies have shown that mechanical properties of the bed are key to explaining the observed speed-up. Here we used a combination of the latest remote sensing datasets between 1996 and 2016, data assimilation tools and numerical perturbation experiments to quantify the relative importance of all processes in driving the recent changes in Pine Island Glacier dynamics. We show that (1) calving and ice shelf thinning have caused a comparable reduction in ice-shelf buttressing over the past two decades, that (2) simulated changes in ice flow over a viscously deforming bed are only compatible with observations if large and widespread changes in ice viscosity and/or basal slipperiness are taken into account, and that (3) a spatially varying, predominantly plastic bed rheology can closely reproduce observed changes in flow without marked variations in ice-internal and basal properties. Our results demonstrate that in addition to its evolving ice thickness, calving processes and a heterogeneous bed rheology play a key role in the contemporary evolution of Pine Island Glacier.

Description: The UN Sustainable Development Goals (SDGs) and the Paris Agreement have ushered in a new era of policymaking to deliver on the formulated goals. Energy policies are key to ensuring universal access to affordable, reliable, sustainable, and modern energy (SDG7). Yet they can also have considerable impact on other goals. To successfully achieve multiple goals concurrently, policies need to balance different objectives and manage their interactions. Refining previously contemplated design principles, we identify three key principles - complementary, transparency and adaptability - as highly pertinent for multiple-objective energy policies based on a synthesis of seventeen coordinated policy case studies. First, policies should entail complementary measures and design provisions that specifically target non-energy objectives (complementarity). Second, policy impacts should be tracked comprehensively in both energy and non-energy domains to uncover diminishing returns and facilitate policy learning (transparency). Third, policies should be capable of adapting to changing objectives over time (adaptability). These principles are rarely considered in current policies, implying the need to mainstream them into the next generation of policymaking by pointing to best practices and new tools.

Description: Agent-based models are a natural choice for modeling complex social systems. In such models simple stochastic interaction rules for a large population of individuals on the microscopic scale can lead to emergent dynamics on the macroscopic scale, for instance a sudden shift of majority opinion or behavior. Here we are introducing a methodology for studying noise-induced tipping between relevant subsets of the agent state space representing characteristic configurations. Due to a large number of interacting individuals, agent-based models are high-dimensional, though usually a lower-dimensional structure of the emerging collective behaviour exists. We therefore apply Diffusion Maps, a non-linear dimension reduction technique, to reveal the intrinsic low-dimensional structure. We characterize the tipping behaviour by means of Transition Path Theory, which helps gaining a statistical understanding of the tipping paths such as their distribution, flux and rate. By systematically studying two agent-based models that exhibit a multitude of tipping pathways and cascading effects, we illustrate the practicability of our approach.

Description: Abstract. Climate policy analysis needs reference scenarios to assess emissions targets and current trends. When presenting their national climate policies, countries often showcase their target trajectories against fictitious so-called baselines. These counterfactual scenarios are meant to present future Greenhouse Gas (GHG) emissions in the absence of climate policy. These so-called baselines presented by countries are often of limited use as they can be exaggerated and the methodology used to derive them is usually not transparent. Scenarios created by independent modeling groups using integrated assessment models (IAMs) can provide different interpretations of several socio-economic storylines and can provide a more realistic backdrop against which the projected target emission trajectory can be assessed. However, the IAMs are limited in regional resolution. This resolution is further reduced in intercomparison studies as data for a common set of regions are produced by aggregating the underlying smaller regions. Thus, the data are not readily available for country-specific policy analysis. This gap is closed by downscaling regional IAM scenarios to country-level. The last of such efforts has been performed for the SRES scenarios (Special Report on Emissions Scenarios), which are over a decade old by now. CMIP6 scenarios have been downscaled to a grid, however they cover only a few combinations of forcing levels and SSP storylines with only a single model per combination. Here, we provide up to date country scenarios, downscaled from the full RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process. The data is available at https://doi.org/10.5281/zenodo.3638137 (Gütschow et al., 2020).

Description: Elizabeth Ferris’ review of research on environmental change and human mobility in this colloquium points to the important role that development actors play in identifying potential solutions for affected persons. She mentions in particular the work of the World Bank’s Climate Change Group and the Global Knowledge Partnership on Migration and Development (KNOMAD). Such mobility is indeed a critical issue from a development perspective, as reflected in the Sustainable Development Goals. Goals 10.7. and 13 encourage states to ‘facilitate orderly, safe, regular and responsible migration and mobility of people’ and demand ‘urgent action to combat climate change and its impacts’, with a focus on enhancing mitigation, adaptation, and disaster risk reduction practices. The World Bank’s development goals of eradicating extreme poverty and boosting shared prosperity further recognize the need to build capacity in these areas.

Description: The stringency of the EU's Emission Trading System (ETS) is bound to be ratcheted-up to deliver on more ambitious goals as formulated in the EU's Green Deal. Tightening the cap needs to consider the interactions with the Market Stability Reserve (MSR), which will be reviewed in 2021. We analyse these issues using the model LIMES-EU. First, we examine how revising MSR parameters impacts allowance cancellations. We find that varying key design parameters leads to cancellations in the range of 2.6–7.9 Gt – compared to 5.1 Gt under current regulation. Overall, the bank thresholds, which define when there is intake to/outtake from the MSR, have the highest impact. Intake rates above 12% only have a limited effect, and cause oscillatory intake behaviour. Second, we analyse how more ambitious climate 2030 targets can be achieved by adjusting the linear reduction factor (LRF). We find that the LRF increases MSR cancellations substantially up to 10.0 Gt. This implies that increasing its value from currently 2.2% to only 2.6% could be consistent with an EU-wide target of −55% by 2030. However, MSR cancellations are subject to large uncertainty, which increases the complexity of the market and induces high price uncertainty.

Description: Climate policy needs to set incentives for actors who face imperfect, distorted markets and large uncertainties about the costs and benefits of abatement. Investors price uncertain assets according to their expected return and risk (carbon beta). We study carbon pricing and financial incentives in a consumption-based asset pricing model distorted by technology spillover and time-inconsistency. We find that both distortions reduce the equilibrium asset return and delay investment in abatement. However, their effect on the carbon beta and risk premium of abatement can be decreasing (when innovation spillovers are not anticipated) or increasing (when climate policy is not credible). Efficiency can be restored by carbon pricing and financial incentives, implemented in our model by a regulator and by a long-term investment fund. The regulator commands carbon pricing and the fund provides subsidies to reduce technology costs or to boost investment returns. The investment subsidy creates a financial incentive that complements the carbon price. In this way the investment fund can support climate policy when the actions of the regulator fall short. These instruments must also consider the investment risk and the sequence of their implementation. The investment fund can then pave the way for carbon pricing in later periods by preventing a capital misallocation that would be too expensive to correct. Thus the investment fund improves the feasibility of ambitious carbon pricing.

Description: The past and future evolution of the Antarctic Ice Sheet is largely controlled by interactions between the ocean and floating ice shelves. To investigate these interactions, coupled ocean and ice sheet model configurations are required. Previous modelling studies have mostly relied on high resolution configurations, limiting these studies to individual glaciers or regions over short time scales of decades to a few centuries. We present a framework to couple the dynamic ice sheet model PISM with the global ocean general circulation model MOM5 via the ice-shelf cavity module PICO. Since ice-shelf cavities are not resolved by MOM5, but parameterized with the box model PICO, the framework allows the ice sheet and ocean model to be run at resolution of 16 km and 3 degree, respectively. This approach makes the coupled configuration a useful tool for the analysis of interactions between the entire Antarctic Ice Sheet and the Earth system over time spans on the order of centuries to millennia. In this study we describe the technical implementation of this coupling framework: sub-shelf melting in the ice sheet model is calculated by PICO from modeled ocean temperatures and salinities at the depth of the continental shelf and, vice versa, the resulting mass and energy fluxes from the melting at the ice-ocean interface are transferred to the ocean model. Mass and energy fluxes are shown to be conserved to machine precision across the considered model domains. The implementation is computationally efficient as it introduces only minimal overhead. The framework deals with heterogeneous spatial grid geometries, varying grid resolutions and time scales between the ice and ocean model in a generic way, and can thus be adopted to a wide range of model setups.