ALBERT

Description: Droughts can impact on forest functioning and production, and even lead to tree mortality. However, drought is an elusive phenomenon that is difficult to quantify and define universally. In this study, we assessed the performance of a set of indicators that have been used to describe drought conditions in the summer months (June, July, August) over a 30 year period (1981–2010) in Finland. Those indicators include the Standardized Precipitation Index (SPI), the Standardized Precipitation–Evapotranspiration Index (SPEI), the Soil Moisture Index (SMI) and the Soil Moisture Anomaly (SMA). Herein, regional soil moisture was produced by the land surface model JSBACH. While SPI, SPEI, and SMA show a degree of anomalies from the statistical means over a period, SMI is directly connected to plant available water and closely dependent on soil properties. Moreover, the buffering effect of soil moisture and the associated soil moisture memory can impact on the onset and duration of drought as indicated by the SMI and SMA, whereas SPI and SPEI are directly controlled by meteorological conditions. In particular, we investigated whether the SMI, SMA and SPEI are able to indicate the Extreme Drought affecting Forest health (EDF) in Finland. EDF thresholds for these indicators are suggested, based on the spatially representative statistics of forest health observations in the exceptional dry year 2006. Our results showed that SMI was the best indicator in capturing the spatial extent of forest damage induced by the extreme drought in 2006. In addition, the derived thresholds were applied to those indicators to capture EDF events over the summer months of the 30 year study period. The SPEI and SMA showed more frequent EDF events over the 30 year period, and typically described a higher fraction of influenced area than SMI. In general, the suggested EDF thresholds for those indicators may be used for the indication of EDF events in Finland or other boreal forests areas in the context of future climate scenarios. However, the results have to be interpreted carefully, with due consideration of their different properties and the complexity of drought. Our results would suggest that in order to take appropriate precautions to mitigate against possible forest losses, an integrated analysis of projected drought with drought indicators is recommended.

Description: Permafrost or perennially frozen ground is an important part of the terrestrial cryosphere; roughly one quarter of Earth's land surface is underlain by permafrost. The impact of the currently observed warming, which is projected to persist during the coming decades due to anthropogenic CO2 input, certainly has effects for the vast permafrost areas of the high northern latitudes. The quantification of these effects, however, is scientifically still an open question. This is partly due to the complexity of the system, where several feedbacks are interacting between land and atmosphere, sometimes counterbalancing each other. Moreover, until recently, many global circulation models (GCMs) and Earth system models (ESMs) lacked the sufficient representation of cold region physical soil processes in their land surface schemes, especially of the effects of freezing and thawing of soil water for both energy and water cycles. Therefore, it will be analysed in the present study how these processes impact large-scale hydrology and climate over northern hemisphere high latitude land areas. For this analysis, the atmosphere-land part of MPI-ESM, ECHAM6-JSBACH, is driven by prescribed observed SST and sea ice in an AMIP2-type setup with and without newly implemented cold region soil processes. Results show a large improvement in the simulated discharge. On one hand this is related to an improved snowmelt peak of runoff due to frozen soil in spring. On the other hand a subsequent reduction of soil moisture leads to a positive land atmosphere feedback to precipitation over the high latitudes, which reduces the model’s wet biases in precipitation and evapotranspiration during the summer. This is noteworthy as soil moisture – atmosphere feedbacks have previously not been in the research focus over the high latitudes. These results point out the importance of high latitude physical processes at the land surface for the regional climate.

Description: Permafrost or perennially frozen ground is an important part of the terrestrial cryosphere; roughly one quarter of Earth's land surface is underlain by permafrost. The currently observed global warming is most pronounced in the Arctic region and is projected to persist during the coming decades due to anthropogenic CO2 input. This warming will certainly have effects on the ecosystems of the vast permafrost areas of the high northern latitudes. The quantification of such effects, however, is still an open question. This is partly due to the complexity of the system, including several feedback mechanisms between land and atmosphere. In this study we contribute to increasing our understanding of such land–atmosphere interactions using an Earth system model (ESM) which includes a representation of cold-region physical soil processes, especially the effects of freezing and thawing of soil water on thermal and hydrological states and processes. The coupled atmosphere–land models of the ESM of the Max Planck Institute for Meteorology, MPI-ESM, have been driven by prescribed observed SST and sea ice in an AMIP2-type setup with and without newly implemented cold-region soil processes. Results show a large improvement in the simulated discharge. On the one hand this is related to an improved snowmelt peak of runoff due to frozen soil in spring. On the other hand a subsequent reduction in soil moisture enables a positive feedback to precipitation over the high latitudes, which reduces the model's wet biases in precipitation and evapotranspiration during the summer. This is noteworthy as soil-moisture–atmosphere feedbacks have previously not been the focus of research on the high latitudes. These results point out the importance of high-latitude physical processes at the land surface for regional climate.

Description: In order to evaluate whether the initialization of soil moisture has the potential to improve the prediction skill of earth system models (ESMs) on seasonal to decadal timescales, an elaborate experiment was conducted. For this task a coupled land–atmosphere model with prescribed ocean was utilized. The experiment design considered soil moisture initialization in different seasons and years and yielded information about the lifetime (memory) of extreme yet realistic soil moisture perturbations. Our analyses were focused on root zone soil moisture (RootSM) as it comprises the part of the soil that directly interacts with the atmosphere via bare-soil evaporation and transpiration. We found that RootSM memory differs not only spatially but also depends on the time of initialization. A long memory of up to 1 year is evident mostly for dry soil moisture regimes after heavy precipitation periods or prior to snow covered conditions. Short memory below 2 weeks prevails in wet soil moisture regimes and prior to distinct precipitation periods or snowmelt. Furthermore, RootSM perturbations affect other land surface states, e.g. soil temperature and leaf carbon content, and even induce anomalies with specific memory in these variables. Especially for deep-layer soil temperature, these anomalies can last for up to several years. As long as RootSM memory is evident, we found that anomalies occur periodically in other land surface states whenever climate conditions allow for interactions between that state and RootSM. Additionally, anomaly recurrence is visible for RootSM itself. This recurrence is related to the thickness of the soil layer below the root zone and can affect RootSM for several years. From our findings we conclude that soil moisture initialization has the potential to improve the predictive skill of climate models on seasonal scales and beyond. However, a sophisticated, multilayered soil hydrology scheme is necessary to allow for the interactions between RootSM and the deep-soil layer reservoir.

Description: In order to evaluate whether the initialization of soil moisture has the potential to improve the prediction skill of coupled climate models at seasonal to decadal time scales, an elaborated AMIP-type experiment was conducted. The experiment design considered soil moisture initialization in different seasons and years, and yields information about the life-time (memory) of extreme yet realistic soil moisture perturbations. Our analyses were focused on root zone soil moisture (RootSM) as it comprises the part of the soil that directly interacts with the atmosphere via bare soil evaporation and transpiration. We found that RootSM memory differs not only spatially but also depends on the time of initialization. Long memory up to one year is evident mostly for dry soil moisture regimes, after heavy precipitation periods or prior to snow covered conditions. Short memory below two weeks prevails in wet soil moisture regimes and prior to distinct precipitation periods or snow melt. Furthermore, RootSM perturbations affect other land surface states, e.g. soil temperature and leaf carbon content, and even induce anomalies with specific memory in these variables. Especially for deep layer soil temperature these anomalies can last up to several years. As long as RootSM memory is evident, we found that anomalies occur periodically in other land surface states whenever climate conditions allow for interactions between that state and RootSM. Additionally, anomaly recurrence is visible for RootSM itself. This recurrence is related to the thickness of the soil layer below the root zone and can affect RootSM for several years. From our findings we conclude that soil moisture initialization has the potential to improve the predictive skill of climate models on seasonal scales and beyond. However, a sophisticated, multi-layered soil hydrology scheme is necessary, to allow for the interactions between RootSM and the deep soil layer reservoir.

Description: The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (“LMIP”, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (“LFMIP”, building upon the GLACE-CMIP blueprint).

Description: Droughts can have an impact on forest functioning and production, and even lead to tree mortality. However, drought is an elusive phenomenon that is difficult to quantify and define universally. In this study, we assessed the performance of a set of indicators that have been used to describe drought conditions in the summer months (June, July, August) over a 30-year period (1981–2010) in Finland. Those indicators include the Standardized Precipitation Index (SPI), the Standardized Precipitation–Evapotranspiration Index (SPEI), the Soil Moisture Index (SMI), and the Soil Moisture Anomaly (SMA). Herein, regional soil moisture was produced by the land surface model JSBACH of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM). Results show that the buffering effect of soil moisture and the associated soil moisture memory can impact on the onset and duration of drought as indicated by the SMI and SMA, while the SPI and SPEI are directly controlled by meteorological conditions. In particular, we investigated whether the SMI, SMA and SPEI are able to indicate the Extreme Drought affecting Forest health (EDF), which we defined according to the extreme drought that caused severe forest damages in Finland in 2006. The EDF thresholds for the aforementioned indicators are suggested, based on the reported statistics of forest damages in Finland in 2006. SMI was found to be the best indicator in capturing the spatial extent of forest damage induced by the extreme drought in 2006. In addition, through the application of the EDF thresholds over the summer months of the 30-year study period, the SPEI and SMA tended to show more frequent EDF events and a higher fraction of influenced area than SMI. This is because the SPEI and SMA are standardized indicators that show the degree of anomalies from statistical means over the aggregation period of climate conditions and soil moisture, respectively. However, in boreal forests in Finland, the high initial soil moisture or existence of peat often prevent the EDFs indicated by the SPEI and SMA to produce very low soil moisture that could be indicated as EDFs by the SMI. Therefore, we consider SMI is more appropriate for indicating EDFs in boreal forests. The selected EDF thresholds for those indicators could be calibrated when there are more forest health observation data available. Furthermore, in the context of future climate scenarios, assessments of EDF risks in northern areas should, in addition to climate data, rely on a land surface model capable of reliable prediction of soil moisture.

Description: We developed a coupled regional climate system model based on the CCLM regional climate model. Within this model system, using OASIS3-MCT as a coupler, CCLM can be coupled to two land surface models (the Community Land Model (CLM) and VEG3D), the NEMO-MED12 regional ocean model for the Mediterranean Sea, two ocean models for the North and Baltic seas (NEMO-NORDIC and TRIMNP+CICE) and the MPI-ESM Earth system model.We first present the different model components and the unified OASIS3-MCT interface which handles all couplings in a consistent way, minimising the model source code modifications and defining the physical and numerical aspects of the couplings. We also address specific coupling issues like the handling of different domains, multiple usage of the MCT library and exchange of 3-D fields.We analyse and compare the computational performance of the different couplings based on real-case simulations over Europe. The usage of the LUCIA tool implemented in OASIS3-MCT enables the quantification of the contributions of the coupled components to the overall coupling cost. These individual contributions are (1) cost of the model(s) coupled, (2) direct cost of coupling including horizontal interpolation and communication between the components, (3) load imbalance, (4) cost of different usage of processors by CCLM in coupled and stand-alone mode and (5) residual cost including i.a. CCLM additional computations.Finally a procedure for finding an optimum processor configuration for each of the couplings was developed considering the time to solution, computing cost and parallel efficiency of the simulation. The optimum configurations are presented for sequential, concurrent and mixed (sequential+concurrent) coupling layouts. The procedure applied can be regarded as independent of the specific coupling layout and coupling details.We found that the direct cost of coupling, i.e. communications and horizontal interpolation, in OASIS3-MCT remains below 7 % of the CCLM stand-alone cost for all couplings investigated. This is in particular true for the exchange of 450 2-D fields between CCLM and MPI-ESM. We identified remaining limitations in the coupling strategies and discuss possible future improvements of the computational efficiency.

Description: An adapted Earth system model is used to investigate the limitations that future climate and water availability impose on the potential expansion and productivity of croplands. The model maximises the cropland area under prevailing climate conditions and accounts for an optimized, sustainable irrigation practice, thus allowing to consider the two-way feedback between climate and agriculture. We show that the total cropland area could be extended substantially throughout the 21st century, especially in South America and sub-Saharan Africa, where the rising water demand resulting from increasing temperatures can largely be met by increasing precipitation and irrigation rates. When accounting for the CO2 fertilization effect, only few agricultural areas have to be abandoned, while increasing temperatures allow to expand croplands even into high northern latitudes. Without the CO2 fertilization effect there is no increase in the overall cropland fraction during the second half of the century but areal losses in increasingly water-stressed regions can be compensated by an expansion in regions, previously too cold. However, global yields are more sensitive and, without the benefits of CO2 fertilization, they may decrease when green house gas concentrations exceed the RCP4.5 scenario. For certain regions the situation is even more concerning and guaranteeing food security in dry areas in Northern Africa, the Middle East and South Asia will become increasingly difficult, even under the most optimistic assumptions.

Description: Processes driving the production, transformation and transport of methane (CH4) in wetland ecosystems are highly complex. Thus, serious challenges are constitutes in terms of the mechanistic process understanding, the identification of potential environmental drivers and the calculation of reliable CH4 emission estimates. We present a simple calculation algorithm to separate open-water CH4 fluxes measured with automatic chambers into diffusion- and ebullition-derived components, which helps facilitating the identification of underlying dynamics and potential environmental drivers. Flux separation is based on ebullition related sudden concentration changes during single measurements. A variable ebullition filter is applied, using the lower and upper quartile and the interquartile range (IQR). Automation of data processing is achieved by using an established R-script, adjusted for the purpose of CH4 flux calculation. The algorithm was tested using flux measurement data (July to September 2013) from a former fen grassland site, converted into a shallow lake as a result of rewetting ebullition and diffusion contributed 46 and 55 %, respectively, to total CH4 emissions, which is comparable to those previously reported by literature. Moreover, the separation algorithm revealed a concealed shift in the diurnal trend of diffusive fluxes throughout the measurement period.