ALBERT

Description: Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Description: About one-half of the global CO2 emissions from fossil fuel combustion and deforestation accumulates in the atmosphere, where it contributes to global warming. The rest is taken up by vegetation and the ocean. The precise contribution of the two sinks, and their location and year-to-year variability are, however, not well understood. We use two different approaches, batch Bayesian synthesis inversion and variational data assimilation, to deduce the global spatiotemporal distributions of CO2 fluxes during 2009-2010. One of our objectives is to assess different sources of uncertainties in inferred fluxes, including uncertainties in prior flux estimates and observations, and differences in inversion techniques. For prior constraints, we utilize fluxes and uncertainties from the CASA-GFED model of the terrestrial biosphere and biomass burning driven by satellite observations and interannually varying meteorology. We also use measurement-based ocean flux estimates and two sets of fixed fossil CO2 emissions. Here, our inversions incorporate column CO2 measurements from the GOSAT satellite (ACOS retrieval, filtered and bias-corrected) and in situ observations (individual flask and afternoon-average continuous observations) to estimate fluxes in 108 regions over 8-day intervals for the batch inversion and at 3 x 3.75 weekly for the variational system. Relationships between fluxes and atmospheric concentrations are derived consistently for the two inversion systems using the PCTM atmospheric transport model driven by meteorology from the MERRA reanalysis. We compare the posterior fluxes and uncertainties derived using different data sets and the two inversion approaches, and evaluate the posterior atmospheric concentrations against independent data including aircraft measurements. The optimized fluxes generally resemble those from other studies. For example, the results indicate that the terrestrial biosphere is a net CO2 sink, and a GOSAT-only inversion suggests a shift in the global sink from the tropics south to the north relative to the prior and to an in-situ-only inversion. We also find a smaller terrestrial sink in higher-latitude northern regions in boreal summer of 2010 relative to 2009.

Description: Satellite observations and numerical modeling studies have demonstrated that the Asian Summer Monsoon (ASM) can provide a conduit for gas-phase pollutants in south Asia to reach the lower stratosphere. Now, observations from the CALIPSO satellite have revealed the Asian Tropopause Aerosol Layer (ATAL), a summertime accumulation of aerosols associated with ASM anticyclone, in the upper troposphere and lower stratosphere (UTLS). The ATAL has potential implications for regional cloud properties, climate, and chemical processes in the UTLS. Here, we show in situ measurements from balloon-borne instrumentation, aircraft and satellite observations, combined with trajectory and chemical transport model (CTM) simulations to explore the origin, composition, physical and optical properties of aerosols in the ATAL. In particular, we show balloon-based observations from our BATAL-2015 field campaign to India and Saudi Arabia in summer 2015, including in situ backscatter measurements from COBALD instruments, and some of the first observations of size and volatility of aerosols in the ATAL layer using optical particle counters (OPCs). Back trajectory calculations initialized from CALIPSO observations point to deep convection over North India as a principal source of ATAL aerosols. Available aircraft observations suggest significant sulfur and carbonaceous contributions to the ATAL, which is supported by simulations using the GEOS-Chem CTM. Source elimination studies conducted with the GEOS-Chem indicate that 80-90% of ATAL aerosols originate from south Asian sources, in contrast with some earlier studies.

Description: Earth's climate is changing and will continue to change into the foreseeable future. Expected changes in the climatological distribution of precipitation, surface temperature, and surface solar radiation will significantly impact agriculture. Adaptation strategies are, therefore, required to reduce the agricultural impacts of climate change. Climate change projections of precipitation, surface temperature, and surface solar radiation distributions are necessary input for adaption planning studies. These projections are conventionally constructed from an ensemble of climate model simulations (e.g., the Coupled Model Intercomparison Project 5 (CMIP5)) as an equal weighted average, one model one vote. Each climate model, however, represents the array of climate-relevant physical processes with varying degrees of fidelity influencing the projection of individual climate variables differently. Presented here is a new approach, termed the "Intelligent Ensemble, that constructs climate variable projections by weighting each model according to its ability to represent key physical processes, e.g., precipitation probability distribution. This approach provides added value over the equal weighted average method. Physical process metrics applied in the "Intelligent Ensemble" method are created using a combination of NASA and NOAA satellite and surface-based cloud, radiation, temperature, and precipitation data sets. The "Intelligent Ensemble" method is applied to the RCP4.5 and RCP8.5 anthropogenic climate forcing simulations within the CMIP5 archive to develop a set of climate change scenarios for precipitation, temperature, and surface solar radiation in each USDA Farm Resource Region for use in climate change adaptation studies.

Description: This reply is in response to Vance et al (2017), who expressed concern that their Law Dome summer sea salt record (LDsss; Vance et al 2013) and two Interdecadal Pacific Oscillation (IPO) reconstructions (PLF and DT-median; Vance et al 2015) were not compared properly in our recent study (Palmer et al 2015) describing the eastern Australian and New Zealand summer Drought Atlas (ANZDA) and that this omission mischaracterizes their records.

Description: Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Description: Agricultural production across eastern Australia and New Zealand is highly vulnerable to drought, but there is a dearth of observational drought information prior to CE (Christian Era) 1850. Using a comprehensive network of 176 drought-sensitive tree-ring chronologies and one coral series, we report the first Southern Hemisphere gridded drought atlas extending back to CE 1500. The austral summer (December-February) Palmer drought sensitivity index reconstruction accurately reproduces historically documented drought events associated with the first European settlement of Australia in CE 1788, and the leading principal component explains over 50 percent of the underlying variance. This leading mode of variability is strongly related to the Interdecadal Pacific Oscillation tripole index (IPO), with a strong and robust antiphase correlation between (1) eastern Australia and the New Zealand North Island and (2) the South Island. Reported positive, negative, and neutral phases of the IPO are consistently reconstructed by the drought atlas although the relationship since CE 1976 appears to have weakened.

Description: Satellite observations and numerical modeling studies have demonstrated that the Asian Summer Monsoon (ASM) provide a conduit for gas-phase pollutants in south Asia to reach the lower stratosphere. Now, observations from the CALIPSO satellite have revealed the Asian Tropopause Aerosol Layer (ATAL), a summertime accumulation of aerosols in the upper troposphere and lower stratosphere (UTLS), associated with the ASM anticyclone. The ATAL has potential implications for regional cloud properties, climate, and chemical processes in the UTLS. Here, we show in situ measurements from balloon-borne instruments, aircraft, and satellite observations, together with trajectory and chemical transport model (CTM) simulations to explore the origin, composition, physical, and optical properties of aerosols in the ATAL. In particular, we show balloon-data from our BATAL-2015 field campaign to India and Saudi Arabia in summer 2015, which includes in situ backscatter measurements from COBALD instruments, and the first observations of size and volatility of aerosols in the ATAL layer using optical particle counters (OPCs). Back trajectory calculations initialized from CALIPSO observations point to deep convection over North India as a principal source of ATAL aerosols. Available aircraft observations suggest significant sulfur and carbonaceous components to the ATAL, which is supported by simulations using the GEOS-Chem CTM. Source elimination studies conducted with the GEOS-Chem indicate that ATAL aerosols originate primary from south Asian sources, in contrast with some earlier studies.