Downwelling Shortwave Radiation Research Articles

Shortwave Radiative Flux Variability Through the Lens of the Pacific Decadal Oscillation

AbstractThe variability of the shortwave radiative fluxes at the surface and top of atmosphere (TOA) is examined in a pre‐industrial modeling setup using the Pacific Decadal Oscillation (PDO) as a possible pacemaker of atmospheric decadal‐scale variability. Within models from the Coupled Model Intercomparison Project—Phase 6, downwelling shortwave radiation at the surface, the net shortwave fluxes at the surface and TOA, as well as cloud radiative effects show remarkably similar patterns associated with the PDO. Through ensemble simulations designed with a pure PDO pattern in the North Pacific only, we show that the PDO relates to about 20%–40% of the unforced year‐to‐year variability of these shortwave fluxes over the Northern Hemispheric continents. The sea surface temperature imprint on shortwave‐flux variability over land is larger for spatially aggregated time series as compared to smaller areas, due to the blurring effect of small‐scale atmospheric noise. The surface and TOA radiative flux anomalies associated with the PDO index range of [−1.64; 1.64] are estimated to reach up to ±6 Wm−2 for North America, ∓3 Wm−2 for India and ±2 Wm−2 for Europe. We hypothesize that the redistribution of clouds in response to a North Pacific PDO anomaly can impact the South Pacific and North Atlantic SSTs.

Divergent responses of subtropical evergreen and deciduous forest carbon cycles to the summer 2022 drought

Ongoing shifts in climate, especially extreme drought, is posing considerable threats to the forest carbon uptake worldwide. In China, summer 2022 was the warmest and driest since the beginning of meteorological measurements. This study synthesized the tower-based carbon fluxes and climate data from two subtropical evergreen and deciduous forest ecosystems to investigate the effects of such summer drought. Interestingly, the net ecosystem production (NEP = −NEE) only exhibited a slight decrease at the deciduous forest while it even enhanced at the evergreen forest during the summer 2022 drought. Further analysis revealed that although reductions in gross primary productivity (GPP) and ecosystem respiration (R e) were found at both sites, larger decrease in R e than GPP at the evergreen forest led to stronger NEP compared to the previous year. However, the NEP of two forest ecosystems sharply reduced in the following 2023, which can be ascribed to the legacy effects of the summer 2022 drought. The results of multiple linear regression revealed that soil water content (SWC) was recognized as the primary driver of GPP and R e, and downwelling shortwave radiation (R g) regulated the variability of NEP during the summer 2022. Across these forest carbon fluxes including GPP, R e and NEP, the deciduous forest exhibited larger resistance, whereas the evergreen forest showed stronger resilience. All analyses emphasized the diverse adaptive strategies among vegetation types, which acted an important role in assessing ecosystem carbon sequestration in face of future climate change.

Continental-scale bias-corrected climate and hydrological projections for Australia

Abstract. The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. The NHP aimed to provide nationally consistent hydrological projections across jurisdictional boundaries to support planning of water-dependent industries. NHP is complementary to those previously produced by federal and state governments, universities, and other organisations for limited geographical domains. The projections comprise an ensemble of application-ready bias-corrected climate model data, derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099, and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau of Meteorology's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs), and one method was applied to a regional climate model (RCM) forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation, and surface winds. These variables are required inputs to the Bureau of Meteorology's landscape water balance hydrological model (AWRA-L), which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration, and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation, and examples of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (https://doi.org/10.25914/6130680dc5a51, Bureau of Meteorology, 2021), and a user interface is accessible at https://awo.bom.gov.au/products/projection/ (last access: 24 November 2023).

Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Reanalysis, and Deep Neural Network Postprocessing

AbstractThis study developed and evaluated 30‐m daily evapotranspiration (ET) estimates using the Priestley‐Taylor Jet Propulsion Laboratory (PT‐JPL) model with ECOSTRESS, Moderate MODIS, harmonized Landsat Sentinel‐2 (HLS) imagery, ERA5‐Land reanalysis, and eddy covariance measurements. The new daily 30‐m ET showed significantly improved performance (overall, r = 0.8, RMSE = 1.736, KGE = 0.466) at 145 EC sites over contiguous United States compared to the current 70‐m ECOSTRESS ET (overall, r = 0.485, RMSE = 4.696, KGE = −0.841). A deep neural network postprocessing model trained with ET measurements from EC sites further improved the performance on test sites that were not used for model training (overall, r = 0.842, RMSE = 0.88, KGE = 0.792). The 30‐m ET estimation biases were significantly related to the biases in the upwelling longwave (RUL) and downwelling shortwave radiation (RDS) inputs, with ET estimates driven by MODIS radiation showing higher biases compared to those driven by ERA5‐Land radiation. The error diagnosis using random forest indicates that ET biases tend to be larger under higher ET estimates, and RUL and RDS were the primary contributors to the high bias at the higher ET ranges, with partial dependence plots revealing that the estimation biases tend to be higher under more humid environment, denser vegetation covers, and high net radiation conditions. In conclusion, higher spatial resolution satellite imagery of vegetation characteristics and higher temporal resolution radiation data, combined with continent‐wide EC measurements and deep learning, provided substantial added value for improving ET estimations at the field scale (30‐m).

Subseasonal forecast skill of evaporative demand, soil moisture, and flash drought onset from two dynamic models over the contiguous United States

Abstract Flash droughts are rapid developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ETo), and relevant flash drought (FD) indices derived from two dynamic models (GEOSV2p1 and GEFSv12) in the Subseasonal Experiment (SubX) project between years 2000-2019 against three reference datasets: MERRA-2, NLDAS-2, and GEFSv12 reanalysis. ETo and its forcing variables at lead week 1 have moderate to high anomaly correlation coefficient (ACC) skill (~0.70-0.95) except downwelling shortwave radiation, and by weeks 3-4 predictability was low for all forcing variables (ACC <0.5). RZSM (0-100cm) for model GEFSv12 showed high skill at lead week 1 (~0.7-0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3-4, better than ETo forecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2 and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ~0.6 for RZSM forecasts between lead weeks 3-4. Analysis of major FD events reveal that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability.

GOES-R land surface products at Western Hemisphere eddy covariance tower locations

The terrestrial carbon cycle varies dynamically on hourly to weekly scales, making it difficult to observe. Geostationary (“weather”) satellites like the Geostationary Environmental Operational Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence. The Advanced Baseline Imager (ABI) aboard GOES-R measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. However, GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature (LST), and downwelling shortwave radiation (DSR) at 314 ABI fixed grid pixels containing eddy covariance towers. We demonstrate how to best combine satellite and in-situ datasets and show how ABI attributes useful for ecosystem monitoring vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.

2020–2021年金佛山亚热带常绿阔叶林碳水通量观测数据集

Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station was officially included in the construction list of the selected national field stations in December, 2020 by China’s Ministry of Science and Technology. In its main observation field, based on the eddy covariance technique, long-term and continuous measurements of carbon and water fluxes in the subtropical evergreen broad-leaved forest ecosystem has been carried out since three years ago. Using the flux data processing system of ChinaFLUX, we conducted the strict work of data quality control and evaluation and obtained the standard dataset of carbon and water fluxes and the key meteorological data in the subtropical evergreen broad-leaved forest ecosystem at the Jinfo Station from 2020 to 2021. The dataset was comprised of net ecosystem carbon exchange, gross primary productivity, ecosystem respiration, latent heat, sensible heat, as well as air temperature/humidity, wind speed/direction, soil temperature/humidity, precipitation, downwelling shortwave radiation, net radiation and photosynthetically active radiation. This dataset can provide significant data support for analyzing the carbon source/sink of typical forest ecosystems, exploring the coupling relation between carbon and water cycles, as well as their response mechanisms to global climate change.

Determination of the Availability and Variation of Surface Downwelling Shortwave Radiation in Saudi Arabia Using EUMETSAT Satellite Imagery

Solar energy is expected to be a viable alternative sustainable energy source in the near future due to diminishing fossil fuel resources and escalating changes in the climate. Surface Downwelling Shortwave (SDS) radiation is an important component for the characterization of energy deposition of the earth’s surface. The monthly means of SDS radiation for Saudi Arabia were extracted from EUMETSAT (SARAH-2.1 climate data record) between 1983 and 2022. The country experienced the lowest level (188 ± 40 W m-2) of SDS radiation in the winter season with a range of 67-271 W m-2. The summer season in Saudi Arabia has the highest level of radiation with a mean value of 304± 27 W m-2, which is 1.62 times the mean of the winter season. The region experienced the highest (10.6 W m-2/10y) and lowest (-10.80 W m-2/10y) levels of a statistically significant upward and downward trend in SDS radiation during the spring and summer seasons respectively in the first decade of the 21st century (2001-2010). During the spring season, there was an upward trend in the availability of SDS radiation across the region, with a magnitude of 3.8 W m-2/10y with a 99% confidence level. In contrast, there was a downward trend in SDS radiation in the summer with a magnitude of 1.0 W m-2/10y with a 90% confidence level followed by the autumn season when the trend was lowest (-0.45 W m-2/10y) and statistically non-significant.

Validation and Spatiotemporal Analysis of Surface Net Radiation from CRA/Land and ERA5-Land over the Tibetan Plateau

High spatial–temporal resolution surface net radiation (RN) data are of great significance to the study of climate, ecology, hydrology and cryosphere changes on the Tibetan Plateau (TP), but the verification of the surface net radiation products on the plateau is not sufficient. In this study, the China Meteorological Administration Global Land Surface Reanalysis Products (CRA/Land) and ECMWF Land Surface Reanalysis version 5 (ERA5-Land) RN data were validated using ground measurements at daily and monthly time scales, and the spatiotemporal patterns were also analyzed. The results indicate the following: (1) CRA/Land overestimated while ERA5-Land underestimated RN, but CRA/Land RN outperformed ERA5-Land in observations at the daily and monthly scale. (2) The CRA/Land RN data had a larger error in the central part and a smaller error in the northeast of the TP, while ERA5-Land showed the opposite. (3) The spatial patterns of RN revealed by CRA/Land and ERA5-Land data showed differences in most regions. The CRA/Land data showed that the RN of the TP had a downward trend during 2000 and 2020 with a slope of −0.112 W·m−2/a, while the ERA5-Land data indicated an upward trend with a change rate of 0.016 W·m−2/a. (4) Downwelling shortwave radiation (DSR), upwelling shortwave radiation (USR), downwelling longwave radiation (DLR) and upwelling longwave radiation (ULR) are the four components of RN, and the evaluation results indicate that the DSR, DLR and ULR recorded via CRA/Land and ERA5-Land are consistent with the observed data, but the consistency between the USR recorded via CRA/Land and ERA5-Land and the observed data is poor. (5) The inconsistency of the USR data is the main reason for the large differences in the spatiotemporal distribution of CRA/Land and ERA5-Land RN data across the TP.

Estimation of 1 km downwelling shortwave radiation over the Tibetan Plateau under all-sky conditions

Abstract. Downwelling shortwave radiation (DSR) is the basic driving force for the energy and water cycles of the Earth's climate system. Called the Third Pole of the Earth, the Tibetan Plateau (TP) absorbs a large amount of shortwave radiation and exerts important impacts on global weather and climate change. However, due to coarse spatial resolution and insufficient consideration of factors influencing radiative transfer processes, DSR parameterization schemes still need to be improved when applied to the TP. Based on satellite datasets and meteorological forcing data, all-sky DSR over the TP at a spatial resolution of 1 km was derived using an improved parameterization scheme. The influence of topography and different radiative attenuations were comprehensively taken into account. Specifically, the introduction of cloud multiscattering and topography factors further improves the DSR estimation accuracy. The validation results indicated that the developed parameterization scheme showed reasonable accuracy. By comparing with current, widely used DSR products based on the same in situ observations, the derived DSR performed much better on different spatial and temporal scales. On instantaneous, 10 d and monthly timescales, the root-mean-square errors (RMSEs) of the derived DSR are 132.8–158.2, 70.8–76.5 and 61.3–67.5 W m−2, respectively, which are much smaller than those of current DSR products. The derived DSR not only captured the temporal-variation characteristics that are more consistent with the in situ measurements, but also provided reasonable spatial patterns. Meanwhile, the proposed parameterization scheme demonstrated its superiority in characterizing more details and high dynamics of the spatial pattern of DSR due to its terrain correction and high resolution. Moreover, this parameterization scheme does not need any local correction in advance and has the potential to be extended to other regions in the world.

The potential global distribution of an emerging forest pathogen, Lecanosticta acicola, under a changing climate

Brown spot needle blight (BSNB), caused by Lecanosticta acicola (Thüm.) Syd., is an emerging forest disease of Pinus species originating from North America and introduced to Europe and Asia. Severity and spread of the disease has increased in the last two decades in North America and Europe as a response to climate change. No modeling work on spread, severity, climatic suitability, or potential distribution has been done for this important emerging pathogen. This study utilizes a global dataset of 2,970 independent observations of L. acicola presence and absence from the geodatabase, together with Pinus spp. distribution data and 44 independent climatic and environmental variables. The objectives were to (1) identify which bioclimatic and environmental variables are most influential in the distribution of L. acicola; (2) compare four modeling approaches to determine which modeling method best fits the data; (3) examine the realized distribution of the pathogen under climatic conditions in the reference period (1971–2000); and (4) predict the potential future global distribution of the pathogen under various climate change scenarios. These objectives were achieved using a species distribution modeling. Four modeling approaches were tested: regression-based model, individual classification trees, bagging with three different base learners, and random forest. Altogether, eight models were developed. An ensemble of the three best models was used to make predictions for the potential distribution of L. acicola: bagging with random tree, bagging with logistic model trees, and random forest. Performance of the model ensemble was very good, with high precision (0.87) and very high AUC (0.94). The potential distribution of L. acicola was computed for five global climate models (GCM) and three combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (SSP-RCP): SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5. The results of the five GCMs were averaged on combined SSP-RCP (median) per 30-year period. Eight of 44 studied factors determined as most important in explaining L. acicola distribution were included in the models: mean diurnal temperature range, mean temperature of wettest quarter, precipitation of warmest quarter, precipitation seasonality, moisture in upper portion of soil column of wettest quarter, surface downwelling longwave radiation of driest quarter, surface downwelling shortwave radiation of warmest quarter and elevation. The actual distribution of L. acicola in the reference period 1971–2000 covered 5.9% of Pinus spp. area globally. However, the model ensemble predicted potential distribution of L. acicola to cover an average of 58.2% of Pinus species global cover in the reference period. Different climate change scenarios (five GCMs, three SSP-RCPs) showed a positive trend in possible range expansion of L. acicola for the period 1971–2100. The average model predictions toward the end of the century showed the potential distribution of L. acicola rising to 62.2, 61.9, 60.3% of Pinus spp. area for SSP1-RCP2.6, SSP2-RCP4.5, SSP5-RCP8.5, respectively. However, the 95% confidence interval encompassed 35.7–82.3% of global Pinus spp. area in the period 1971–2000 and 33.6–85.8% in the period 2071–2100. It was found that SSP-RCPs had a little effect on variability of BSNB potential distribution (60.3–62.2% in the period 2071–2100 for medium prediction). In contrast, GCMs had vast impact on the potential distribution of L. acicola (33.6–85.8% of global pines area). The maps of potential distribution of BSNB will assist forest managers in considering the risk of BSNB. The results will allow practitioners and policymakers to focus surveillance methods and implement appropriate management plans.

Photovoltaic power resource at the Atacama Desert under climate change

The Atacama desert is a region with exceptional conditions for solar power production. However, despite its relevance, the impact of climate change on this resource in this region has barely been studied. Here, we use regional climate models to explore how climate change will affect the photovoltaic solar power resource per square meter (PVres) in Atacama.Models project average reductions in PVres of 1.5% and 1.7% under an RCP8.5 scenario, respectively, for 2021-2040 and 2041-2060. Under RCP2.6 and the same periods, reductions range between 1.2% and 0.5%. Also, we study the contribution to future changes in PVres of the downwelling shortwave radiation, air temperature and wind velocity. We find that the contribution from changes in wind velocity is negligible. Future changes of downwelling shortwave radiation, under the RCP8.5 scenario, cause up to 87% of the decrease of PVres for 2021-2040 and 84% for 2041-2060. Rising temperatures due to climate change are responsible for drops in PVres ranging between 13%–19% under RCP2.6 and 14%–16% under RCP8.5.We conclude that climate change has the potential to impact the PVres in the Atacama region while retaining exceptional conditions for solar power production.

BESSv2.0: A satellite-based and coupled-process model for quantifying long-term global land–atmosphere fluxes

Recent remote-sensing-based global carbon, water and energy budgets over land still include considerable uncertainties. Most existing flux products of terrestrial carbon, water and energy components were developed individually, despite the inherently coupled processes among them. In this study, we present a new set of global daily surface downwelling shortwave radiation (SW), net radiation (Rnet), evapotranspiration (ET), gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) datasets at 0.05° resolutions from 1982 to 2019, by improving a satellite-based and coupled-process model—the Breathing Earth System Simulator (BESS). The new version of BESS (v2.0) integrated a newly developed ecosystem respiration module, an optimality-based maximum carboxylation rate (Vcmax) model, and extended the temporal coverage of flux datasets from 1982 to 2019. We evaluated BESS products against the FLUXNET2015 dataset at the site scale, and against several remote sensing and/or machine learning products on a global scale. At the site scale, BESS products agreed well with FLUXNET measurements, capturing 84%, 53%, 65%, 51% and 31% of daily variation in Rnet, ET, GPP, TER and NEE, respectively. Interannual variation in BESS NEE showed relatively low consistency with the FLUXNET measurements, while the rest fluxes explained approximately half of the interannual variation. On a global scale, we found marked discrepancies in spatio-temporal patterns between BESS and several benchmark products. Over the period 1982–2019, BESSv2.0 estimated the mean annual global Rnet, ET, GPP, TER and NEE to be 340.97 ± 5.22 ZJ yr−1 (mean ± 1 SD), 67.67 ± 0.71 103 km3 yr−1, 125.74 ± 5.95 Pg C yr−1, 109.30 ± 3.16 Pg C yr−1, and − 16.28 ± 2.95 Pg C yr−1, respectively, with significant annual linear trends (P < 0.01) by −0.05 103 km3 yr−2 for ET, by 0.52 Pg C yr−2 for GPP, by 0.28 Pg C yr−2 for TER, and by −0.25 Pg C yr−2 for NEE. We further evaluated various coupled processes derived by BESS in terms of functional properties (i.e., Budyko relation, carbon-use efficiency, water-use efficiency, and light-use efficiency), which agreed well with FLUXNET observations, unlike the benchmark products. Overall, BESSv2.0 can serve as a set of reliable and independent products from other global satellite products, facilitating studies related to global carbon, water and energy budgets in a coupled and comprehensive manner.

Internal Variability of the Climate System Mirrored in Decadal‐Scale Trends of Surface Solar Radiation

AbstractDecadal‐scale unforced climate variability associated with low‐frequency ocean (or coupled) modes can be identified within the time‐evolving sea surface temperature (SST) pattern. We seek to find a relationship between 12 well‐known climate modes of variability (El Niño–Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, etc.), which are reflected in the SST pattern, and decadal trends in downwelling shortwave radiation at Earth's surface (surface solar radiation, surface shortwave radiation [SSR]). The analysis is performed using the pre‐industrial control runs (piControl) of 57 models from the Coupled Model Intercomparison Project—Phase 6. We find that regional SSR trends occur during periods when a climate mode is transitioning and show a mirroring pattern (opposite sign SSR trends) when the mode transitions in the opposite direction. Unforced trends in clear‐sky SSR are mostly driven by SST‐induced water vapor changes, while all‐sky SSR trends show a complex spatial structure with trends of different signs in different regions. Unforced SSR trends are generally weaker in simulations with climatological SSTs. The role of natural aerosols as another potentially relevant factor for SSR variability is briefly addressed and we find that constantly higher aerosol loads result in increased variability for clear‐sky conditions, but not for all‐sky conditions. The results from this study suggest that all‐sky dimming and brightening in different parts of the world are enhanced rather than suppressed by internal variability related to an SST‐pattern with the exception of India and China, where both effects are present. A practical application can be the planning of photovoltaic energy facilities given areas where internal variability is generally smaller or affects SSR in opposite directions.

Future projections of wind and solar energy resources over China from regional climate models based on bias correction

In this study, the wind and solar energy resources over China are projected during 2031–2060 under the Representative Concentration Pathway (RCP) 8.5 scenario with the Weather Research and Forecasting (WRF) model and Regional Climate Model version 4 (RegCM4). The bias correction method based on probability distribution is applied to correct the simulated 100-m wind speed and surface down-welling shortwave radiation. The results show that the bias correction method can remarkably improve the skill of the individual simulations and the ensemble mean in capturing the features of wind speed and shortwave radiation from the aspects of spatial distribution, annual cycle, quantile distribution, probability distribution and cumulative probability distribution. The future projections from the bias-corrected ensemble show that both the wind and solar energy resources are abundant over northwestern China, with higher wind power density reaching up to 130 W m−2 in winter and richer surface down-welling shortwave radiation above 240 W m−2 in summer. Generally, the wind and solar energy resources will decrease in the future, by around 6% over the western regions and 4% over southeastern China in winter, respectively. The interannual variability of the two resources is projected to increase over most areas of northern China, indicating a decrease in the stability of resources, while tends to decrease over Southeastern China.

Spatio-temporal variation of evapotranspiration and its linkage with environmental factors in the largest freshwater lake wetland in China

Study regionPoyang lake wetland Study focusEvapotranspiration (ET) is a critical parameter in the hydrologic and energy budget, and it is important for understanding spatio-temporal variations in ET in large lake wetlands. Monthly ET in the Poyang Lake wetland during 2008–2017 was estimated by the remote sensing ET retrieval model based on a Nonparametric approach (RS-NP). And the spatio-temporal variation of ET and the influencing factors were analyzed. New hydrological insights for the regionThe validation revealed the usefulness of the RS-NP model in a moist region and yielded a relative error of 6 % (12 %) in the wet (dry) season. Temporally, the average yearly ET was 884 mm with ET/P (precipitation) of 55 %− 65 %. The monthly ET/P ratio increased significantly from 40 %− 50 % from January to May to 90 %− 130 % from July to October. ET in June was lower by over 20 % than that in May and July with the lowest ET/P value (33 %). Spatially, relatively larger and lower ET occurred in the northern and southern parts, respectively. The main factors affecting ET variability were downwelling shortwave radiation (∼2/3 contribution) and water area (∼1/3 contribution) at the monthly scale. Precipitation and surface temperature dominantly controlled the hysteresis effects with a lag of one month and regulated LE monthly variations. This study improves our understanding of complicated water-atmosphere interactions and their linkages with environmental factors in large lake systems.

How does the phytoplankton–light feedback affect the marine N2O inventory?

Abstract. The phytoplankton–light feedback (PLF) describes the interaction between phytoplankton biomass and the downwelling shortwave radiation entering the ocean. The PLF allows the simulation of differential heating across the ocean water column as a function of phytoplankton concentration. Only one third of the Earth system models contributing to the 6th phase of the Coupled Model Intercomparison Project (CMIP6) include a complete representation of the PLF. In other models, the PLF is either approximated by a prescribed climatology of chlorophyll or not represented at all. Consequences of an incomplete representation of the PLF on the modelled biogeochemical state have not yet been fully assessed and remain a source of multi-model uncertainty in future projection. Here, we evaluate within a coherent modelling framework how representations of the PLF of varying complexity impact ocean physics and ultimately marine production of nitrous oxide (N2O), a major greenhouse gas. We exploit global sensitivity simulations at 1∘ horizontal resolution over the last 2 decades (1999–2018), coupling ocean, sea ice and marine biogeochemistry. The representation of the PLF impacts ocean heat uptake and temperature of the first 300 m of the tropical ocean. Temperature anomalies due to an incomplete PLF representation drive perturbations of ocean stratification, dynamics and oxygen concentration. These perturbations translate into different projection pathways for N2O production depending on the choice of the PLF representation. The oxygen concentration in the North Pacific oxygen-minimum zone is overestimated in model runs with an incomplete representation of the PLF, which results in an underestimation of local N2O production. This leads to important regional differences of sea-to-air N2O fluxes: fluxes are enhanced by up to 24 % in the South Pacific and South Atlantic subtropical gyres but reduced by up to 12 % in oxygen-minimum zones of the Northern Hemisphere. Our results, based on a global ocean–biogeochemical model at CMIP6 state-of-the-art level, shed light on current uncertainties in modelled marine nitrous oxide budgets in climate models.

Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition

Abstract. Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snowpack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

Reducing Southern Ocean Shortwave Radiation Errors in the ERA5 Reanalysis with Machine Learning and 25 Years of Surface Observations

Abstract Earth system models struggle to simulate clouds and their radiative effects over the Southern Ocean, partly due to a lack of measurements and targeted cloud microphysics knowledge. We have evaluated biases of downwelling shortwave radiation in the ERA5 climate reanalysis using 25 years (1995–2019) of summertime surface measurements, collected on the Research and Supply Vessel (RSV) Aurora Australis, the Research Vessel (R/V) Investigator, and at Macquarie Island. During October–March daylight hours, the ERA5 simulation of SWdown exhibited large errors (mean bias = 54 W m−2, mean absolute error = 82 W m−2, root-mean-square error = 132 W m−2, and R2 = 0.71). To determine whether we could improve these statistics, we bypassed ERA5’s radiative transfer model for SWdown with machine learning–based models using a number of ERA5’s gridscale meteorological variables as predictors. These models were trained and tested with the surface measurements of SWdown using a 10-fold shuffle split. An extreme gradient boosting (XGBoost) and a random forest–based model setup had the best performance relative to ERA5, both with a near complete reduction of the mean bias error, a decrease in the mean absolute error and root-mean-square error by 25% ± 3%, and an increase in the R2 value of 5% ± 1% over the 10 splits. Large improvements occurred at higher latitudes and cyclone cold sectors, where ERA5 performed most poorly. We further interpret our methods using Shapley additive explanations. Our results indicate that data-driven techniques could have an important role in simulating surface radiation fluxes and in improving reanalysis products. Significance Statement Simulating the amount of sunlight reaching Earth’s surface is difficult because it relies on a good understanding of how much clouds absorb and scatter sunlight. Relative to summertime surface observations, the ERA5 reanalysis still overestimates the amount of sunlight entering the Southern Ocean. We taught some models how to predict the amount of sunlight entering the Southern Ocean using 25 years of surface observations and a small set of meteorological variables from ERA5. By bypassing the ERA5’s internal simulation of the absorption and scattering of sunlight, we can drastically reduce biases in the predicted surface shortwave radiation. Large improvements in cold sectors of cyclones and closer to Antarctica were observed in regions where many numerical models struggle to simulate the amount of incoming sunlight correctly.

Identifying links between monsoon variability and rice production in India through machine learning

Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability.