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NASA NTRS2022

Attribution of Stratospheric and Tropospheric Ozone Changes Between 1850 and 2014 in CMIP6 Models

We quantify the impacts of halogenated ozone-depleting substances (ODSs), greenhouse gases (GHGs), and short-lived ozone precursors on ozone changes between 1850 and 2014 using single-forcing perturbation simulations from several Earth system models with interactive chemistry participating in the Coupled Model Intercomparison Project Aerosol and Chemistry Model Intercomparison Project. We present the responses of ozone to individual forcings and an attribution of changes in ozone columns and vertically resolved stratospheric and tropospheric ozone to these forcings. We find that whilst substantial ODS-induced ozone loss dominates the stratospheric ozone changes since the 1970s, in agreement with previous studies, increases in tropospheric ozone due to increases in short-lived ozone precursors and methane since the 1950s make increasingly important contributions to total column ozone (TCO) changes. Increases in methane also lead to substantial extra-tropical stratospheric ozone increases. Impacts of nitrous oxide and carbon dioxide on stratospheric ozone are significant but their impacts on TCO are small overall due to several opposing factors and are also associated with large dynamical variability. The multi-model mean (MMM) results show a clear change in the stratospheric ozone trends after 2000 due to now declining ODSs, but the trends are generally not significantly positive, except in the extra-tropical upper stratosphere, due to relatively small changes in forcing over this period combined with large model uncertainty. Although the MMM ozone compares well with the observations, the inter-model differences are large primarily due to the large differences in the models' representation of ODS-induced ozone depletion.

Halogenated ozone-depleting substances↗

NASA NTRSDate not supplied

Update on AeroCom Biomass Burning Emission Injection Height experiment (BBEIH)

In BBEIH, we introduced the biomass burning injection heights based on MISR stereo-derived plume-height retrievals (Val Martin et al., 2010; 2018) in four participating CTMs. Specifically, we proposed 4 simulations for the year 2008: 1) BASE: using the biomass burning emissions from Global Fire Emissions Database version 4 with small ﬁres (GFED4s) and the model default fire emission injection height; 2) BBIH: Same as BASE, but using the injection height derived from the seasonally and regionally varying MISR-retrieved plume heights; 3) NOBB: no fire emission; 4) Same as BASE, but using Fire Energetics and Emissions Research version 1.0 (FEER 1.0). We will address the following scientific questions: 1) To what extent are the model simulations sensitive to the assumed biomass burning injection height? 2) In which regions/seasons/surface-types are the aforementioned sensitivities most important? We will update the multi-model inter-comparison with a focus on the vertical aerosol distribution in near-source characteristics and downwind plume evolution." In order to test the sensitivity of model results to smoke injection height, we proposed the biomass burning emission injection height experiment (BBEIH) in the AeroCom 2019 workshop. The description of implementation methods and input data for BBEIH can be found in https://wiki.met.no/aerocom/phase3-experiments#biomass_burning_emission_injection_height_experiment_bbeih.

Xiaohua Pan↗

NASA NTRS2022

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD550. Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s−1, the C4C and MODIS AOD550s also overestimate CAOD550 and FAOD550, respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research. even larger than what was diagnosed here. We conclude with a discussion on how

Aerosols↗

NASA NTRS2022

Anthropogenic Sulfate Aerosol Pollution in South and East Asia Induces Increased Summer Precipitation Over Arid Central Asia

Precipitation has increased across the arid Central Asia region over recent decades. However, the underlying mechanisms of this trend are poorly understood. Here, we analyze multi-model simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) to investigate potential drivers of the observed precipitation trend. We find that anthropogenic sulfate aerosols over remote polluted regions in South and East Asia lead to increased summer precipitation, especially convective and extreme precipitation, in arid Central Asia. Elevated concentrations of sulfate aerosols over remote polluted Asia cause an equatorward shift of the Asian Westerly Jet Stream through a fast response to cooling of the local atmosphere at mid-latitudes. This shift favours moisture supply from low-latitudes and moisture flux convergence over arid Central Asia, which is confirmed by a moisture budget analysis. High levels of absorbing black carbon lead to opposing changes in the Asian Westerly Jet Stream and reduced local precipitation, which can mask the impact of sulfate aerosols. This teleconnection between arid Central Asia precipitation and anthropogenic aerosols in remote Asian polluted regions highlights long-range impacts of anthropogenic aerosols on atmospheric circulations and the hydrological cycle.

Atmospheric dynamics↗

NASA NTRS2023

Arctic Tropospheric Ozone: Assessment of Current Knowledge and Model Performance

As the third most important greenhouse gas (GHG) after carbon dioxide (CO2) and methane (CH4), tropospheric ozone (O3) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O3 depletion in the springtime; however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been previously reported, and we show that there continues to be no convergence among models or improved accuracy in simulating tropospheric O3 and its precursor species. The MMM underestimates Arctic surface O3 by 5 % to 15 % depending on the location. The vertical distribution of tropospheric O3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O3 within a range of ±50 % depending on the model and the altitude. The MMM performs best, within ±8 % for most locations and seasons. However, nearly all models overestimate O3 near the tropopause (∼300 hPa or ∼8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O3 at high latitudes. For example, the MMM is biased high by about 20 % at Eureka. Observed and simulated O3 precursors (CO, NOx, and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NOx but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O3 matches the observed O3 reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever and have not increased in accuracy for representing Arctic tropospheric O3.

Arctic↗

NASA NTRS2021

Eastern Equatorial Pacific Warming Delayed By Aerosols and Thermostat Response to Co2 Increase

Understanding the tropical Pacific response to global warming remains challenging. Here, we use a range of CMIP6 greenhouse warming experiments to assess the recent and future evolution of the equatorial Pacific east-west temperature gradient and corresponding Walker circulation. In abrupt CO2-increase scenarios many models generate an initial strengthening of this gradient resembling an ocean thermostat (OT), followed by a small weakening; other models generate an immediate weakening that becomes progressively stronger establishing a pronounced eastern equatorial Pacific (EP) warming pattern. The initial response in these experiments is a strong predictor for the future EP pattern simulated in both abrupt and realistic warming scenarios, but not in historical simulations showing no multi-model trend. The likely explanation is that the recent CO2-driven changes in the tropical Pacific are masked by aerosol effects and a potential OT-related delay, while the EP warming pattern will emerge as greenhouse gases overcome aerosol forcing.

Climate and Earth system modelling↗

NASA NTRS2022

NASA's NMME-Based S2S Hydrologic Forecast System for Food Insecurity Early Warning in Southern Africa

In situ hydrologic monitoring over regions most susceptible to food insecurity can be a challenge in current times due to various socio-economic and political issues in combination with environmental factors such as ongoing famine or drought. Hydrologic monitoring and initializing forecasts based on remotely sensed and analyzed data can contribute significantly to early warning in such regions. Routine hydrologic forecasts, as provided by NASA’s Hydrologic Forecasting and Analysis System (NHyFAS), are a recent addition to early warning systems. A custom instance of NHyFAS, termed FLDAS-Forecast, is used by FEWS NET’s Land Data Assimilation System (FLDAS). The FLDAS-Forecast’s dynamic forecasting component was originally set up with Goddard Earth Observing System (GEOS) forecast inputs and has been recently expanded with precipitation forecast forcing from the North American Multi-Model Ensemble (NMME). This paper describes the improvements in seasonal hydrologic forecasts produced with this updated system. Evaluations in this study focus on soil moisture across southern Africa’s growing season. Soil moisture forecasts are benchmarked and evaluated relative to climatology-based forecasts and historic runs, which are driven by observation-based meteorological forcing fields, and they are verified with remotely sensed observations of soil moisture and vegetation. Through multiple deterministic and probabilistic skill assessments, we show that using the larger ensemble of NMME precipitation inputs in the forecast system results in higher quality hydrologic forecasts than are allowed by climatology- or GEOS-only-based forecasts. Further, the near-real-time NMME-based rootzone soil moisture forecasts were able to correctly predict developing drought conditions over southern Africa through late 2019 and into early 2020.

Hydrologic forecasts↗

NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗

NASA NTRSDate not supplied

Climate change signal in global agriculture emerges earlier in new generation of climate and crop models

Potential climate-related impacts on future crop yield are a major societal concern first surveyed in a harmonized multi-model effort in 2014. We report here on new 21st-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean, and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5 to -6% (SSP126) and +1 to -24% (SSP585) — explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9 shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts — when the change signal emerges from the noise — consistently occurs earlier in the new projections for several main producing regions before 2040. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

climate change↗

NASA NTRSDate not supplied

Warming Reduces the Efficacy of Wet Conditions to Moderate Extreme Heat and Atmospheric Aridity Across the Central Plains

In the literature, dry land surface conditions possess a well-established association with extreme heat and high atmospheric aridity, especially in regions that experience strong land-atmosphere feedbacks. However, few studies have examined whether global warming will impact the ability of wet conditions to constrain high temperatures and atmospheric aridity occurrences. Here we investigate how wet conditions, which we define using precipitation and soil moisture, constrain high temperature and high vapor pressure deficit during the warm season over the Central Plains in North America at different global warming levels (0-3 degrees K). Using a CMIP6 multi-model ensemble, we show that absolute changes in the maximum temperature distribution outweigh the historical cooling effect of high precipitation or soil moisture conditions, reducing the probability of non-extreme maximum temperatures under global warming. Strikingly, the conditional likelihood of non-extreme maximum temperatures falls from 97-98% at baseline conditions to 50-58% at 1 degree of global warming. Once 2 degrees of warming is reached, there is very little probability of constraining extreme maximum temperatures. On the other hand, high precipitation and high soil moisture are able to better retain the ability to constrain concurrent high vapor pressure deficit conditions – moving from 99% at baseline conditions to 81-91% at 1 degree of warming and 26-42% at 2 degrees of warming. With our results, we find that the extent of this retention is dependent on how individual models represent relative changes in the VPD distribution as temperatures warm. Overall, our results highlight the growing vulnerability of the Central North American region to warmer temperatures and drier atmospheric conditions, even during periods of high precipitation and soil moisture.

Extreme heat↗

NASA NTRSDate not supplied

The Response of the Quasi-Biennial Oscillation to Increased CO and its Modulation by Composition Feedbacks

The Quasi-Biennial Oscillation (QBO) impacts the large-scale circulation by altering lower stratospheric meridional temperature gradients which alter the propagation of upward propagating planetary waves. Proposed teleconnections of the QBO include impacts on the stratospheric polar vortices, extratropical surface winter climate, and the Madden-Julian Oscillation. However, long-term projections of the QBO remain highly uncertain. While recent multi-model investigations (Richter et al. 2019, Butchart et al. 2020) show that the amplitude of the QBO weakens robustly among models, changes in QBO period and stability remain highly uncertain. Here we examine the QBO response to increased greenhouse gases using the NASA Goddard Institute for Space Studies Middle Atmosphere Model E2.2 (Rind et al., 2020; Orbe et al. 2020). Compared to lower vertical resolution versions of ModelE, E2.2 also has a higher model top (0.002 hPa) and employs additional interactive non-orographic gravity wave drag sources from convection and shear, which produce a sufficiently realistic QBO, thus rendering it suitable for use in climate change studies. Overall, we find that both the QBO period and amplitude decrease in response to increased CO , the former related to increased lower stratospheric momentum fluxes (associated with convection) and the latter associated partly with a stronger residual mean circulation. Experiments integrated using fully interactive chemistry also reveal that ozone feedbacks significantly impact the magnitude of the QBO amplitude response. Finally, integrations using fixed (pre-industrial) sea surface temperatures (SST) show that the QBO amplitude responds differently to rapid adjustments versus SST feedbacks, as compared to the QBO period.

Quasi-Biennial Oscillation↗

NASA NTRSDate not supplied

Dynamical Sensitivity in Response to a Wide Range of Abrupt CO 2 Forcings

An improved understanding of dynamical variability is important for projecting future changes in extratropical weather and the interaction between the extratropical troposphere and the tropics, stratosphere, cryosphere and ocean. Despite their simplicity, the abrupt 2x- and 4xCO 2 forcing simulations from the Coupled Model Intercomparison Project (CMIP) Phase 6 DECK experiments enable a mechanistic look into the forcing and feedback response characteristics of models that can be unambiguously attributed to an increase in carbon dioxide concentrations. Thus, while typically used to evaluate the climate sensitivity in models, with a primary focus on global surface temperature change, here we focus on several measures of extratropical variability, including projected changes in the storm tracks and in stratospheric polar vortex variability. Results are primarily based on simulations produced using low- and high-top versions of the NASA Goddard Institute for Space Studies Model (ModelE) but results are also presented from the larger CMIP6 multi-model ensemble. In addition to the 2x- and 4xCO 2 simulations, we also explore the linearity of the response of extratropical dynamical variability in ModelE to varying levels of CO 2 spanning the range 1/8-8xCO 2 . In particular, we show that the expansion of the jet streams varies nonlinearly with increasing CO 2 , especially in the Northern Hemisphere, and that this can be interpreted in terms of variations in ocean heat transport. The impact of composition feedbacks on changes in variability is also discussed.

dynamical variability↗

NASA NTRS2023

The Hydrological Cycle and Ocean Circulation of the Maritime Continent in the Mid-Pliocene: Results From PlioMIP2

The Maritime Continent (MC) forms the western boundary of the tropical Pacific Ocean, and relatively small changes in this region can impact the climate locally and remotely. In the mid-Piacenzian warm period of the Pliocene (mPWP; 3.264 to 3.025 Ma) atmospheric CO2 concentrations were ∼ 400 ppm, and the subaerial Sunda and Sahul shelves made the land–sea distribution of the MC different to today. Topographic changes and elevated levels of CO2, combined with other forcings, are therefore expected to have driven a substantial climate signal in the MC region at this time. By using the results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), we study the mean climatic features of the MC in the mPWP and changes in Indonesian Throughflow (ITF) with respect to the preindustrial. Results show a warmer and wetter mPWP climate of the MC and lower sea surface salinity in the surrounding ocean compared with the preindustrial. Furthermore, we quantify the volume transfer through the ITF; although the ITF may be expected to be hindered by the subaerial shelves, 10 out of 15 models show an increased volume transport compared with the preindustrial. In order to avoid undue influence from closely related models that are present in the PlioMIP2 ensemble, we introduce a new metric, the multi-cluster mean (MCM), which is based on cluster analysis of the individual models. We study the effect that the choice of MCM versus the more traditional analysis of multi-model mean (MMM) and individual models has on the discrepancy between model results and data. We find that models, which reproduce modern MC climate well, are not always good at simulating the mPWP climate anomaly of the MC. By comparing with individual models, the MMM and MCM reproduce the preindustrial sea surface temperature (SST) of the reanalysis better than most individual models and produce less discrepancy with reconstructed sea surface temperature anomalies (SSTA) than most individual models in the MC. In addition, the clusters reveal spatial signals that are not captured by the MMM, so that the MCM provides us with a new way to explore the results from model ensembles that include similar models.

Hydrological cycle↗

NASA NTRS2023

Heat Stress to Jeopardize Crop Production in the US Corn Belt Based on Downscaled CMIP5 Projections

CONTEXT Global food security faces increasing challenges from the changing climate. Changes of agricultural output from some of the most productive regions such as the US Corn Belt can largely affect the world's food market. Developing predictive understanding of the agricultural risk of climate change and potential mitigation strategies is critical for the global food security. OBJECTIVE The objective of this study is to assess the responses of maize and soybean yield to projected climate changes in the Corn Belt, identify the shifting environment stressors on crop yield, and tackle potential climate adaptation strategies. METHODS We drive a process-based model, the Decision Support System for Agrotechnology Transfer, with high-resolution statistically downscaled and bias-corrected historical and future climates from ten CMIP5 models in the MACA-2 database. RESULTS AND CONCLUSIONS The multi-model ensemble mean suggests a 12% decrease of maize yield by mid-century and 40% by late century, with a high degree of model consensus in the direction of changes; for individual models, the projected decrease of maize yield by late century ranges from <5% to over 80%, with the worst crop outcome corresponding to the most sensitive climate models. Soybean yield is projected to increase by midcentury with a high degree of model consensus, but such consensus is lost by late century as some projections shift to significant decreases. Crop yield in the Corn Belt is currently limited by water stress, but is projected to be increasingly limited by heat stress as well after the midcentury. The mounting heat stress will drive the most productive zone for maize to shift from central to northern part of the Corn Belt, but the projected increase in the northern states cannot fully compensate for the decrease in the south, causing the total production to decrease if agricultural practice stays the same. Earlier planting can alleviate only a small fraction of the heat-induced crop loss in a warmer climate. Climate change will (at least partially) offset the yield boost caused by agricultural technology and intensification. SIGNIFICANCE This study advances our predictive understanding of crop yield responses to climate change, and suggests that a multitude of strategies will be needed to address the climate change challenges for the U.S. agriculture.

Crop yield↗

NASA NTRS2023

The Statistical Emulators of GGCMI Phase 2: Responses of Year-to-Year Variation of Crop Yield to CO 2 , Temperature, Water and Nitrogen Perturbations

Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO 2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO 2 –temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Climate change↗

NASA NTRS2023

Anomalous Net Biome Exchange Over Amazonian Rainforests Induced by the 2015/16 El Niño: Temperature-dominated Total Flux but Soil Dryness-Shaped Spatial Pattern

The magnitude and spatial pattern of anomalous net biome exchange (NBE) induced by the 2015/16 El Niño over Amazonian rainforests remain uncertain. We here investigated them using multi-model posterior NBE products in the Orbiting Carbon Observatory-2 (OCO-2) version 10 modeling intercomparison project. Results suggest that relative to the annual NBE average in 2017/18, larger anomalous carbon release occurred over the eastern and northern Amazonian rainforests in 2015/16, with a total flux of approximately 0.4 PgC yr−1 after assimilating satellite-observed column CO2 concentrations (XCO2) over land. We further find that this anomalous spatial pattern was predominantly determined by soil dryness, while the total positive NBE anomaly was dominated by higher temperature with its contribution of approximately 68～70%. We believe that atmospheric inversions assimilating more satellite-observed XCO2 in future can provide us more comprehensive understanding how Amazonian rainforests cope with the abiotic stresses induced by El Niño events.

Jun Wang↗

NASA NTRS2023

Observational Constraints Reduce Model Spread but Not Uncertainty in Global Wetland Methane Emission Estimates

The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Kuang-Yu Chang↗

NASA NTRS2016

Uncertainty of Wheat Water Use: Simulated Patterns and Sensitivity to Temperature and CO2

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50 of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.

Sensitivity↗