Global Climate Model Simulations Research Articles

Assessment of the Paris urban heat island in ERA5 and offline SURFEX-TEB (v8.1) simulations using the METEOSAT land surface temperature product

Abstract. Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This urgently demands for accurate urban climate projections to help organizations and individuals to make climate-smart decisions. However, most of the large ensembles of global and regional climate model simulations do not include sophisticated urban parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation reanalysis from the European Centre for Medium-Range Weather Forecasts) and in a simulation with the SURFEX (Surface Externalisée) land surface model employing the widely used bulk bare rock approach. The city of Paris is considered as a case study. Subsequently, we apply a more complex urban scheme – SURFEX coupled to the Town Energy Balance (TEB) urban canopy model to assess its benefits on characterizing the Paris urban climate. Both simulations and ERA5 were compared to the LSA SAF (Satellite Application Facility on Land Surface Analysis) land surface temperature product to evaluate the simulation of Parisian surface urban heat island (SUHI). Our results show a significant added value of SURFEX-TEB in reproducing the SUHI during the daytime and the UHI during both the daytime and nighttime (with overall reductions in the bias and root mean square error and improvements in the representation of the statistics of the SUHI/UHI displayed by the Perkins skill score or S score). The improvement in the simulated SUHI is lower during the nighttime due to the lack of land–atmosphere feedbacks in the proposed offline framework. Nonetheless, the offline SURFEX-TEB framework applied here clearly demonstrates the added value of using more comprehensive parameterization schemes to simulate the urban climate and, therefore, allowing the improvement of urban climate projections.

Projecting spatiotemporal changes of precipitation and temperature in Iraq for different shared socioeconomic pathways with selected Coupled Model Intercomparison Project Phase 6

AbstractChanges in precipitation and temperature have crucial implications in the arid region due to their fragile environment. This study was an attempt to estimate possible spatiotemporal alteration of annual and seasonal precipitation and temperature in Iraq. Statistical downscaling of Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) simulations for different shared socioeconomic pathways (SSP) was utilized. The GCMs were ranked according to their skills in simulating climate research unit (CRU) precipitation and temperature climatology along with their seasonality. Nonlocal model output statistics (MOS) models were implemented using a support vector machine (SVM) for downscaling and projection of selected GCM precipitation and temperature. Results revealed ACCESS‐CM2, BCC‐CSM2‐MR, GISS‐E2‐1‐G and MRI‐ESM2‐0 GCMs are most suitable for Iraq. The spatiotemporal changes in precipitation indicated a substantial decrease to north (up to −7.8%·mm−1) while an increase (around 3.0%) to south for different SSPs. Far future (2060–2099) showed both increase and decrease in precipitation than near future (2020–2059). The precipitation was projected to reduce in winter and increase in summer for all climate zones during both periods. The maximum temperature was projected to increase by 4.5°C to the north and 0.9–2°C to the south. In contrast, the minimum temperature was projected to rise by 1.0–3.5°C to both north and south. Both maximum and minimum temperatures may increase; however, more increases might be in winter and less in summer. The minimum temperature increase will be higher than the maximum temperature in the cold northern region and vice versa. Uncertainty in precipitation and temperature projections was higher for the far‐future period with higher SSPs than for the near‐future period with lower SSPs. The results of this study can guide the development of strategic policies for climate resiliency development in Iraq.

Estimated cloud-top entrainment index explains positive low-cloud-cover feedback

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming: The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using state-of-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to -0.41 ± 0.28% K-1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes.

Dansgaard-Oeschger and Heinrich event temperature anomalies in the North Atlantic set by sea ice, frontal position and thermocline structure

We use eighteen timescale-synchronised near-surface temperature reconstructions spanning 10–50 thousand years before present to clarify the regional expression of Dansgaard-Oeschger (D-O) and Heinrich (H) events in the North Atlantic. The North Atlantic Drift region shows D-O temperature variations of ca. 2–5° with Greenland-like structure. The Western Iberian Margin region also shows Greenland-like structure, but with more pronounced surface cooling between interstadials and Heinrich stadials (ca. 6–9 °C) than between interstadials and non-Heinrich stadials (ca. 2–3 °C). The southern Nordic Seas show smaller D-O temperature anomalies (ca. 1–2 °C) that appear out of phase with Greenland. These spatial patterns are replicated in a new global climate model simulation that features unforced (D-O-like) and freshwater forced (H-like) abrupt climate changes. The model simulations and observations suggest consistently that the spatial expression and amplitude of D-O and H event temperature anomalies are dominated by coupled changes in the Atlantic Meridional Overturning, sea ice extent, polar front position and thermocline structure.

Sensitive Dependence of Global Climate to Continental Geometry

AbstractOver its multibillion‐year history, the Earth has experienced a wide range of climates. The long‐term climate is controlled by the atmospheric carbon dioxide concentration, which is regulated by marine sequestration through chemical weathering. This chemical weathering sink is strongly linked to the distribution and composition of the continents. However, the effect of continental distribution has never been studied within a general framework. Here we show that the global weathering rate is sensitive to the size and shape of the continents, but is not well explained by the amount of land in the tropics. We construct synthetic continental configurations and use an ensemble of global climate model simulations to isolate the expected effect of continental arrangement on weathering and carbon burial. Runoff patterns are complex, sensitive to detailed features of continental geometry, and poorly predicted by continental latitude. These results help explain the long‐term variability and irregularity of Earth's climate.

Glacial runoff buffers droughts through the 21st century

Abstract. Global climate model projections suggest that 21st century climate change will bring significant drying in the terrestrial midlatitudes. Recent glacier modeling suggests that runoff from glaciers will continue to provide substantial freshwater in many drainage basins, though the supply will generally diminish throughout the century. In the absence of dynamic glacier ice within global climate models (GCMs), a comprehensive picture of future hydrological drought conditions in glaciated regions has been elusive. Here, we leverage the results of existing GCM simulations and a global glacier model to evaluate glacial buffering of droughts in the Standardized Precipitation-Evapotranspiration Index (SPEI). We compute one baseline version of the SPEI and one version modified to account for glacial runoff changing over time, and we compare the two for each of 56 large-scale glaciated basins worldwide. We find that accounting for glacial runoff tends to increase the multi-model ensemble mean SPEI and reduce drought frequency and severity, even in basins with relatively little (<2 %) glacier cover. When glacial runoff is included in the SPEI, the number of droughts is reduced in 40 of 56 basins and the average drought severity is reduced in 53 of 56 basins, with similar counts in each time period we study. Glacial drought buffering persists even as glacial runoff is projected to decline through the 21st century. Results are similar under representative concentration pathway (RCP) 4.5 and 8.5 emissions scenarios, though results for the higher-emissions RCP 8.5 scenario show wider multi-model spread (uncertainty) and greater incidence of decreasing buffering later in the century. A k-means clustering analysis shows that glacial drought buffering is strongest in moderately glaciated, arid basins.

Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events.

Changing spatial distribution of water flow charts major change in Mars's greenhouse effect.

Early Mars had rivers, but the cause of Mars’s wet-to-dry transition remains unknown. Past climate on Mars can be probed using the spatial distribution of climate-sensitive landforms. We analyzed global databases of water-worked landforms and identified changes in the spatial distribution of rivers over time. These changes are simply explained by comparison to a simplified meltwater model driven by an ensemble of global climate model simulations, as the result of ≳10 K global cooling, from global average surface temperature ≥ 268 K to ~ 258 K, due to a weaker greenhouse effect. In other words, river-forming climates on early Mars were warm and wet first, and cold and wet later. Unexpectedly, analysis of the greenhouse effect within our ensemble of global climate model simulations suggests that this shift was primarily driven by waning non-CO2 radiative forcing, and not changes in CO2 radiative forcing.

Convection-permitting simulations of historical and possible future climate over the contiguous United States

This study presents a novel, high-resolution, dynamically downscaled dataset that will help inform regional and local stakeholders regarding potential impacts of climate change at the scales necessary to examine extreme mesoscale conditions. WRF-ARW version 4.1.2 was used in a convection-permitting configuration (horizontal grid spacing of 3.75 km; 51 vertical levels; data output interval of 15-min) as a regional climate model for a domain covering the contiguous US Initial and lateral boundary forcing for the regional climate model originates from a global climate model simulation by NCAR (Community Earth System Model) that participated in phase 5 of the Coupled Model Inter comparison Project. Herein, we use a version of these data that are regridded and bias corrected. Two 15-year downscaled simulation epochs were examined comprising of historical (HIST; 1990–2005) and potential future (FUTR; 2085–2100) climate using Representative Concentration Pathway (RCP) 8.5. HIST verification against independent observational data revealed that annual/seasonal/monthly temperature and precipitation (and their extremes) are replicated admirably in the downscaled HIST epoch, with the largest biases in temperature noted with daily maximum temperatures (too cold) and the largest biases in precipitation (too dry) across the southeast US during the boreal warm season. The simulations herein are improved compared to previous work, which is significant considering the differences in previous modeling approaches. Future projections of temperature under the RCP 8.5 scenario are consistent with previous works using various methods. Future precipitation projections suggest statistically significant decreases of precipitation across large segments of the southern Great Plains and Intermountain West, whereas significant increases were noted in the Tennessee/Ohio Valleys and across portions of the Pacific Northwest. Overall, these simulations serve as an additional datapoint/method to detect potential future changes in extreme meso-γ weather phenomena.

Projection of Hot and Cold Extremes in the Amu River Basin of Central Asia using GCMs CMIP6

The extreme temperature has become more frequent and intense due to global warming, particularly in dry regions, causing devastating impacts on humans and ecosystems. The transboundary Amu river basin (ARB) is the most vulnerable region in Central Asia (CA) to extreme weather linked to climate change. This study aimed to project warm and cold extremes in ARB for four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and two time-horizons, 2020–2059 and 2060–2099, using daily maximum (Tmax) and minimum temperature (Tmin) simulations of global climate models (GCMs) of Coupled Model Inter-comparison Project phase six (CMIP6). Results revealed that the basin’s west experiences more hot extremes and the east more cold extremes. Climate change would cause a significant increase in the annual mean of Tmax and Tmin. However, the increase in mean Tmin would be much higher (5.0ºC ) than the mean Tmax (4.6ºC ). It would cause an increase in the hot extremes and a decrease in the cold extremes in the basin. The higher increase in the hot extremes would be in the west, while the higher decrease in the cold extreme in the basin’s east. The number of days above 40℃ would increase from 45 to 60 days in the basin’s west and northwest compared to the historical period. The number of days below − 20℃ would decrease up to 45 days in the basin’s east. Overall, the decrease in cold extremes would be much faster than the increase in hot extremes.

On an Improved Second‐Moment Closure Model for Langmuir Turbulence Conditions and Its Application

AbstractRecently, a k‐ω second‐moment closure (SMC) model including the Langmuir turbulence effects was proposed by Yu et al. (2018, https://doi.org/10.1029/2018jc013878), however, the equations for determining the stability functions in it are very complicated. In this study, the equations of the stability functions are derived in more detail and a new explicit version is proposed. In the meantime, a new constant cω4 = 0.15 is introduced into the turbulent frequency transport equation to manifest the role of the shear generating term caused by the Stokes drift. And then the performance of the new SMC model is investigated by one‐dimensional Papa Ocean Station simulation and three‐dimensional global climate model simulation. The results show that the use of the new SMC model produces significant changes in the results of the ocean surface boundary layer simulations at the Papa Ocean Station, especially for the summer and autumn sea surface temperature (SST) simulations, and for the mixed layer depth (MLD) and mixed layer temperature (MLT) in the mixed layer deepening process. In the three‐dimensional simulation, the new SMC model resulted in a decrease of more than 34% in the annual root mean square error of SST in the northern extratropical region compared to the non‐LT effects model. The simulated MLD for tropical regions in winter and summer has also improved by 61.9% and 53.6%, respectively. The effect of the new SMC model on the MLT is mainly found in the regions beyond the tropical in summer and the tropical and northern extratropical regions in winter.

Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region

AbstractDownscaling of global climate model (GCMs) simulations is a key element of regional-to-local-scale climate change projections that can inform impact assessments, long-term planning, and resource management in different sectors. We conduct an intercomparison between statistically and dynamically downscaled GCMs simulations using the hybrid delta (HD) and the Weather Research and Forecast (WRF) Model, respectively, over the Midwest and Great Lakes region to 1) validate their performance in reproducing extreme daily precipitation (P) and daily maximum temperature (Tmax) for summer and winter and 2) evaluate projections of extremes in the future. Our results show the HD statistical downscaling approach, which includes large-scale bias correction of GCM inputs, can reproduce observed extremePandTmaxreasonably well for both summer and winter. However, raw historical WRF simulations show significant bias in both extremePandTmaxfor both seasons. Interestingly, the convection-permitting WRF simulation at 4-km grid spacing does not produce better results for seasonal extremes than the WRF simulation at 12 km using a parameterized convection scheme. Despite a broad similarity for winter extremePprojections, the projected changes in the future summer storms are quite different between downscaling methods; WRF simulations show substantial increases in summer extreme precipitation, while the changes projected by the HD approach exhibit moderate decreases overall. The WRF simulations at 4 km also show a pronounced decoupling effect between seasonal totals and extreme dailyPfor summer, which suggests that there could be more intense summer extremes at two different time scales, with more severe individual convective storms combined with longer summer droughts at the end of the twenty-first century.

Effect of CO2 concentration on drought assessment in China

AbstractGlobal climate change is projected to increase the probability of occurrence and severity of droughts. Increased CO2 concentration drives partial closure of plant stomata and reduces evapotranspiration. However, the impact of reduced evapotranspiration due to CO2 on future droughts characteristics in China is unclear. In this study, we have used the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model simulations (GCMs) under historical period, and four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) to estimate drought. We used a widely applicable drought index (i.e., the self‐calibrating Palmer Drought Severity Index [scPDSI]) to evaluate the historical and future drought conditions, using the Penman–Monteith formula with and without the CO2 concentrations. The results show that the increasing trend of scPDSI slows down when the CO2 effect is considered in all scenarios, especially in Heilongjiang, northern Xinjiang, and the Qinghai–Tibet Plateau. The divergence in the slowdown trend among the different scenarios becomes greater after 2030, with higher emission scenarios implying a greater slowdown in the increasing trend of scPDSI. After considering CO2, drought characteristics such as the drought frequency, intensity, and drought area decrease. Increased CO2 concentration on vegetation physiological processes could mitigate future drought. Therefore, the effects of CO2 on plant physiology should be considered in studies of future drought trends to develop more realistic response and adaptation policies to future drought changes.

IBI-CCS: a regional high-resolution model to simulate sea level in western Europe

Abstract. Projections of coastal sea level (SL) changes are of great interest for coastal risk assessment and decision making. SL projections are typically produced using global climate models (GCMs), which cannot fully resolve SL changes at the coast due to their coarse resolution and lack of representation of some relevant processes (tides, atmospheric surface pressure forcing, waves). To overcome these limitations and refine projections at regional scales, GCMs can be dynamically downscaled through the implementation of a high-resolution regional climate model (RCM). In this study, we developed the IBI-CCS (Iberian–Biscay–Ireland Climate Change Scenarios) regional ocean model based on a 1/12∘ northeastern Atlantic Nucleus for European Modelling of the Ocean (NEMO) model configuration to dynamically downscale CNRM-CM6-1-HR, a GCM with a 1/4∘ resolution ocean model component participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) by the Centre National de Recherches Météorologiques (CNRM). For a more complete representation of the processes driving coastal SL changes, tides and atmospheric surface pressure forcing are explicitly resolved in IBI-CCS in addition to the ocean general circulation. To limit the propagation of climate drifts and biases from the GCM into the regional simulations, several corrections are applied to the GCM fields used to force the RCM. The regional simulations are performed over the 1950 to 2100 period for two climate change scenarios (SSP1-2.6 and SSP5-8.5). To validate the dynamical downscaling method, the RCM and GCM simulations are compared to reanalyses and observations over the 1993–2014 period for a selection of ocean variables including SL. Results indicate that large-scale performance of IBI-CCS is better than that of the GCM thanks to the corrections applied to the RCM. Extreme SLs are also satisfactorily represented in the IBI-CCS historical simulation. Comparison of the RCM and GCM 21st century projections shows a limited impact of increased resolution (1/4 to 1/12∘) on SL changes. Overall, bias corrections have a moderate impact on projected coastal SL changes, except in the Mediterranean Sea, where GCM biases were substantial.

Uncertainties in evapotranspiration projections associated with estimation methods and CMIP6 GCMs for South Korea

This study compared the performance capabilities of three potential evapotranspiration (PET) methods, Thornthwaite (TW), Hargreaves and Samani (HS), and Penman-Monteith (PM), to simulate historical and future daily PET levels in South Korea using climate variables from Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs). Five evaluation metrics were used to quantify the reproducibility of the climate variables and PETs at ten stations in South Korea for the historical period used here (1985–2014). The changes and uncertainty associated with the changes in PET in the near (2031–2060) and far (2071–2100) futures were calculated for two shared socioeconomic pathways (SSPs) of 2–4.5 and 5–8.5. As a result, PETs estimated using the three methods for the historical period showed high performance in terms of five evaluation metrics. Overall, PETs showed an increase for both the future periods and the SSPs. The PET estimated using the PM method showed the greatest increase, while that estimated using HS showed the most modest increase in the future. The PM method also showed the highest reliability and lowest uncertainty in the PET estimations, while the opposite was true for HS. This study contributes to our understanding of rational PET methods by which to calculate hydrological factors such as drought indexes for future periods via GCM simulations.

On the Correspondence Between Atmosphere‐Only and Coupled Simulations for Radiative Feedbacks and Forcing From CO2

AbstractAtmosphere‐only experiments are widely used to investigate climate feedbacks simulated in more computationally expensive fully coupled global climate model simulations. We confirm that this remains a valid approach by comparing the radiative feedbacks and forcing between coupled and atmosphere‐only simulations for the latest models taking part in the 6th phase of the Coupled Model Intercomparison Project (CMIP6). For global‐mean cloud feedbacks, we find a better than previously known correspondence between these experiments, which applies even to the response of individual cloud properties (amount, altitude, and optical depth) and holds even when considering atmosphere‐only simulations of only 1 yr duration. For regional cloud feedbacks, the correspondence between the two experiments is generally present at every geographic location except for the tropical Pacific but takes longer experiments to reveal. For the lapse rate and surface albedo feedbacks, the correspondence between the two experiments is weaker due to the non‐uniform warming pattern and loss of sea ice in the coupled experiment. For the across‐model relationship between 4xCO2 effective radiative forcing and feedback, we find a different behavior across experiments in CMIP6 than in CMIP5, casting doubt on the physical significance of previous results that highlighted an anti‐correlation between the two quantities. Overall, these results confirm the utility of atmosphere‐only experiments particularly to study cloud feedbacks, which are the dominant source of inter‐model spread in climate sensitivity.

The CORDEX-CORE EXP-I Initiative: Description and Highlight Results from the Initial Analysis

Abstract We describe the first effort within the Coordinated Regional Climate Downscaling Experiment–Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of twenty-first-century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over nine CORDEX domains at ∼25-km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, and energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors, and climate service activities. The future of the CORDEX-CORE initiative is also discussed.

Observational evaluation of global climate model simulations of arctic sea ice and adjacent land pertaining to the radiative effects of frozen hydrometeors

This study explores the linkage of frozen hydrometeors (cloud ice and falling ice/snow) with sea ice and adjacent lands through surface energy budget using model-observation comparisons to quantify the roles of the falling ice radiative effects (FIREs) in determining the extent and thickness of Arctic sea ice and adjacent land surface radiation budget and land surface skin (Ts) and surface (SAT) temperatures. The Coupled Model Intercomparison Project Phase 5 (CMIP5) models without FIREs tend to produce underestimated downward longwave radiation, and overestimated shortwave downward radiation and surface albedo, resulting in too-cold skin temperature (TS) and surface air temperature (SAT) and overestimated sea ice concentration (SIC) and thickness (SIT). By comparing two simulations of late 20th Century climate from CESM1-CAM5 model with inclusion and exclusion of FIREs, it is found that TS, SAT, radiation, SIC, and SIT and their seasonal cycles are improved with the inclusion of FIREs. Exclusion of FIREs results in underestimated net downward longwave radiative flux, which is highly correlated with overestimated surface albedo, colder TS, and SAT with a confidence level at 99% (p < 1%). These biases in CESM1-CAM5 resemble those in CMIP5 models without FIREs. With the inclusion of FIREs, the SIC bias is reduced by 2%–15% in summer, while the SIT is improved up to 90% in winter despite little improvement in SIC. These findings suggest a potential link among the increased downward longwave flux, decreased downward shortwave flux, and decreased surface air and land surface temperatures locally, which then drives SIC melting and SIT thinning when FIREs are included relative to when FIREs are excluded. It is suggested that the roles played by FIREs over the Arctic regions are of the same order of importance as those over the Southern Oceans despite the differences in geography and impact of human activity.

Soil carbon losses reduce soil moisture in global climate model simulations

Soil carbon losses reduce soil moisture in global climate model simulations

Soil Carbon Losses Reduce Soil Moisture in Global Climate Model Simulations

Abstract Most agricultural soils have experienced substantial soil organic carbon losses in time. These losses motivate recent calls to restore organic carbon in agricultural lands to improve biogeochemical cycling and for climate change mitigation. Declines in organic carbon also reduce soil infiltration and water holding capacity, which may have important effects on regional hydrology and climate. To explore the regional hydroclimate impacts of soil organic carbon changes, we conduct new global climate model experiments with NASA Goddard Institute for Space Studies ModelE that include spatially explicit soil organic carbon concentrations associated with different human land management scenarios. Compared to a “no land use” case, a year 2010 soil degradation scenario, in which organic carbon content (OCC; weight %) is reduced by a factor of ∼0.12 on average across agricultural soils, resulted in soil moisture losses between 0.5 and 1 temporal standard deviations over eastern Asia, northern Europe, and the eastern United States. In a more extreme idealized scenario where OCC is reduced uniformly by 0.66 across agricultural soils, soil moisture losses exceed one standard deviation in both hemispheres. Within the model, these soil moisture declines occur primarily due to reductions in porosity (and to a lesser extent infiltration) that overall soil water holding capacity. These results demonstrate that changes in soil organic carbon can have meaningful, large-scale effects on regional hydroclimate and should be considered in climate model evaluations and developments. Further, this also suggests that soil restoration efforts targeting the carbon cycle are likely to have additional benefits for improving drought resilience.