Dynamical Downscaling Methods Research Articles

RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension

Abstract. The reduction of in situ observations over the last few decades poses a potential risk of losing important information in regions where local effects dominate the climatology. Reanalyses face challenges in representing climatologies with highly localized effects, especially in regions with complex orography. Empirical downscaling methods offer a cost-effective and easier-to-implement alternative to dynamic downscaling methods and can partially overcome the aforementioned limitations of reanalyses by taking into account the local effects through statistical relationships. This article introduces RASCAL (Reconstruction by AnalogS of ClimatologicAL time series), an open-source Python tool designed to extend time series and fill gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features using the analog method in combination with principal component analysis (PCA). The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allow fast and reproducible downscaling. The evaluations carried out in central Spain, in mountainous and urbanized areas, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis, as expected, in terms of R2, standard deviation, and bias. When analyzing reconstructions against observations, RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations. This confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, as with any other method based on empirical training, this method requires the availability of sufficiently long-term data series. Furthermore, it is susceptible to disruption caused by changes in land use or urbanization processes that might compromise the homogeneity of the training data. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project.

Statistical downscaling of coastal directional wave spectra using deep learning

The modelling of coastal Directional Wave Spectra (DWSs) often requires downscaling techniques integrating DWSs from open ocean boundaries. Dynamic downscaling methods reliant on numerical wave models are often computationally expensive. In coastal areas, wave dynamics are strongly influenced by the bathymetry and coastal morphology, implying that once the DWSs at the open ocean boundary are known, the DWSs at various locations along the coast are almost determined. This property can be utilized for statistical downscaling of coastal DWSs. This study presents a deep learning approach to compute coastal DWSs from open ocean DWSs. The performance of the proposed downscaling model was evaluated using both numerical wave model data and buoy data in the Southern California Bight. The results show that the deep learning approach can effectively and efficiently downscale coastal DWSs without relying on any predefined spectral shapes, thereby showing potential for coastal spectral wave climate studies.

Future Changes in Regional Tropical Cyclone Wind, Precipitation, and Flooding Using Event‐Based Downscaling

AbstractUnderstanding changes in the hazard component of climate risk is important to inform societal resilience planning in a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event‐based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2‐LENS). We find fewer TCs in the future, but these future storms have higher wind speeds and are wetter. We also find that TCs produce heavier 3‐day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200‐year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or the statistical downscaling method. It can also produce quite different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event‐based simulations in the context of downscaling method limitations.

Midsummer precipitation prediction over eastern China by the dynamic downscaling method

AbstractThis study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction.

Dynamical Downscaling of Daily Extreme Temperatures over China Using PRECIS Model

As global warming intensifies and the frequency of extreme weather events rises, posing a major threat to the world’s economy and sustainable development, accurate forecasting of future extreme events is of great significance to mankind’s response to extreme weather events and to the sustainable development of society. Global Climate Models (GCMs) have limitations in their applicability at regional scales due to their coarse resolution. Utilizing dynamical downscaling methods based on regional climate models (RCMs) is an essential approach to obtaining high-resolution climate simulation information in future. This study represents an attempt to extend the use of the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model by employing the BCC-CSM2-MR model from the Beijing Climate Center to drive it, conducting downscaling experiments over China at a spatial resolution of 0.22° (25 km). The simulation and prediction of daily maximum and minimum temperatures across the China region are conducted, marking a significant effort to expand the usage of PRECIS with data from alternative GCMs. The results indicate that PRECIS performs well in simulating the daily maximum and minimum temperatures over the China region, accurately capturing their spatial distribution and demonstrating notable simulation capabilities for both cold and warm regions. In the annual cycle, the simulation performance of PRECIS is superior to its driving GCM, particularly during cold months (i.e., December and from January to May). Regarding future changes, the daily extreme temperatures in most regions are projected to increase gradually over time. In the early 21st century, the warming magnitude is approximately 1.5 °C, reaching around 3 °C by the end of the century, with even higher warming magnitudes exceeding 4.5 °C under the SSP585 scenario. Northern regions will experience greater warming magnitudes than southern regions, suggesting faster increases in extreme temperatures in higher latitudes. This paper provides forecasts of extreme temperatures in China, which will be useful for studying extreme events and for the government to make decisions in response to extreme events.

Investigating transformer‐based models for spatial downscaling and correcting biases of near‐surface temperature and wind‐speed forecasts

AbstractHigh‐resolution and accurate prediction of near‐surface weather parameters based on numerical weather prediction (NWP) models is essential for many downstream and real‐world applications. Traditional dynamical or statistical downscaling methods are insufficient to derive high‐resolution data from operational NWP forecasts, making it essential to devise new approaches. In recent years, an increasing number of researchers have explored the implementations of deep learning (DL) based models for spatial downscaling, motivated by the similarity between the super‐resolution (SR) problem in computer vision (CV) and downscaling. Furthermore, while transformer‐based models have become state‐of‐the‐art models for many SR tasks, they are rarely applied for downscaling of weather forecasts or climate projections. This study adapted transformer‐based models such as SwinIR and Uformer to downscale the temperature at 2 m () and wind speed at 10 m () over Eastern Inner Mongolia, encompassing the area from 39.6–46°N latitude and 111.6–118°E longitude. We used high‐resolution forecast (HRES) data from the European Centre for Medium‐range Weather Forecast (ECMWF) with a spatial resolution of 0.1° as the input and gridded observation data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) at a spatial resolution of 0.01° as the target. Given that the models use observation data rather than a coarse‐grained version of forecast data as the target, they accomplish both bias correction and spatial downscaling. The results demonstrate that the performance of SwinIR and Uformer is superior to that of two convolutional neural network (CNN) based models (UNet and RCAN). Additionally, we introduced a novel module to extract features of varying resolution from the high‐resolution topography data and applied a multiscale feature fusion module to merge features of different scales, contributing to further enhancement of Uformer's performance.

A dynamical downscaling method of groundwater storage changes using GRACE data

Study regionThe Northwest inland region of China. Study focusThe use of Gravity Recovery and Climate Experiment (GRACE) satellite data for assessing groundwater storage changes (GWSC) is rapidly expanding; however, dealing with such low spatial resolution data is always a challenge. In the present study, a dynamical downscaling method based on groundwater flow processes was proposed to simulate GWSC. Firstly, a hypothetical model constructed by MODFLOW package used to evaluate the efficiency of the developed model. Then, a part of the northwest inland region of China was taken as a real example to verify the applicability of the model. Finally, the downscaled results are used to estimate the groundwater storage changes in the study area. New hydrological insights for the regionThe spatiotemporal change of GWSC from the proposed model in a hypothetical conceptual model matched well with those from the MODFLOW package. The average correlation coefficient between downscaled GWSC and observed groundwater level is over 0.54, and downscaled GWSC showed the reasonable heterogeneity in the study area. From 2008–2011, the area with a decline in groundwater storage accounted for about 12% of the total area, while it increased to 21.8% from 2012 to 2016. The proposed model has the potential to be applied for the large-scale evaluation of GWSC and to improve the understanding of GWSC in remote areas.

Projected heat wave increasing trends over China based on combined dynamical and multiple statistical downscaling methods

Extensive investigations on the projection of heat waves (HWs) were conducted on the basis of coarse-resolution global climate models (GCMs). However, these investigations still fail to characterise the future changes in HWs regionally over China. PRECIS dynamical downscaling with a horizontal resolution of 25 km × 25 km was employed on the basis of GCM-HadCM3 to provide reliable projections on HWs over the Chinese mainland, and six statistical downscaling methods were used for bias correction under RCP4.5 and RCP8.5 scenarios. The multi-method ensemble (MME) of the top three dynamical downscaling methods with good performance was used to project future changes. Results showed that PRECIS primarily replicated the detailed spatiotemporal pattern of HWs. However, PRECIS overestimated the HWs in the Northwest and Southeast and expanded the areas of HWs in the Northeast and Southwest. Three statistical downscaling methods (quantile mapping, CDF-t and quantile delta mapping) demonstrated good performance in improving PRECIS simulation for reproducing HWs. By contrast, parametric-based trend-preserving approaches such as scaled distribution mapping and ISI-MIP are outperformed by the three aforementioned methods in downscaling HWs, particularly in the high latitudes of China. Based on MME projections, at the end of the 21st century, the national average of the number of HW days each year, the length of the longest HW event in the year and the extreme maximum temperature in HW will increase by 3 times, 1 time and 1.3 °C, respectively, under the RCP4.5 scenario, whilst that under the RCP8.5 scenario will increase by 8 times, 3 times and 3.7 °C, respectively, relative to 1986–2005. The Northwest is regionally projected to suffer long and hot HWs, whilst the South and Southeast will experience frequent consecutive HWs. Thus, HWs projected by the combined dynamical and statistical downscaling method are highly reliable in projecting HWs over China.

Projection of Future Heatwaves in the Pearl River Delta through CMIP6-WRF Dynamical Downscaling

Abstract Recent worldwide heatwaves have shattered temperature records in many regions. In this study, we applied a dynamical downscaling method on the high-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-1-2-HR) to obtain projections of the summer thermal environments and heatwaves in the Pearl River Delta (PRD) considering three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the middle and late twenty-first century. Results indicated that relative to the temperatures in the 2010s, the mean increases in the summer (June–September) daytime and nighttime temperatures in the 2040s will be 0.7°–0.8°C and 0.9°–1.1°C, respectively. In the 2090s, the mean difference will be 0.5°–3.1°C and 0.7°–3.4°C, respectively. SSP1-2.6 is the only scenario in which the temperatures in the 2090s are expected to be lower than those in the 2040s. When compared with those in the 2010s, hot extremes are expected to be more frequent, intense, extensive, and longer-lasting in the future in the SSP2-4.5 and SSP5-8.5 scenarios. In the 2010s, a heatwave occurred in the PRD lasted for 6 days on average, with a mean daily maximum temperature of 34.4°C. In the 2040s, the heatwave duration and intensity are expected to increase by 2–3 days and 0.2°–0.4°C in all three scenarios. In the 2090s, these values will become 23 days and 36.0°C in SSP5-8.5. Moreover, a 10-yr extreme high temperature in the 2010s is expected to occur at a monthly frequency from June to September in the 2090s. Significance Statement Pearl River Delta (PRD) has been experiencing record-shattering heatwaves in recent years. This study aims to investigate the future trends of summer heatwaves in the PRD by modeling three future scenarios including a sustainable scenario, an intermediate scenario, and a worst-case scenario. Except for the sustainable scenario, summer temperatures in the intermediate and worst-case scenarios will keep increasing, and heatwaves will become more frequent, intense, extensive, and longer-lasting. In the worst-case scenario, extreme heat events that occurred once in 10 years in the 2010s will shorten to once a month in the 2090s. A better understanding of heatwave trends will benefit implementing climate mitigation methods, urban planning, and improving social infrastructure.

Comparison between statistical and dynamical downscaling of rainfall over the Gwadar‐Ormara basin, Pakistan

AbstractThis paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study.

Drought Potential in Borneo Based on the RCP 4.5 Scenario

One of the impacts of climate change is an increase in the frequency and intensity of hydrometeorological disasters such as prolonged droughts. Borneo is one of the areas threatened by drought due to climate change. Therefore, it is important to identify and implement appropriate mitigation and adaptation measures. This study used the dynamical downscaling method by the Conformal Cubic Atmospheric Model (CCAM) to evaluate the potential for drought events in Borneo based on the RCP 4.5 scenario. The annual rainfall in Borneo for 2021-2050 is projected to increase compared to 1991-2020. However, the increase in annual rainfall does not free Borneo from the possibility of drought events in the future. This study’s results indicate that areas in southern Borneo, such as Banjarmasin, Pangkalan Bun, and Pontianak, will have a higher frequency of meteorological drought events and are also expected to experience longer periods of consecutive dry days between 2021-2050 compared to 1991-2020.

Climate change impacts on heating and cooling energy demand in residential building units. Application in the city of Thessaloniki, Greece

The present study aims to evaluate the effect of climate change on the heating and cooling energy needs of a residential building unit, located in the city of Thessaloniki, Greece. Future weather files have been generated using both statistical and dynamical downscaling methods. In the first case, the Meteonorm weather generator was used to generate a future weather file, while for the dynamic downscaling approach, the Hadley Regional Model 3 (HRM3), coupled with Hadley Climate Model 3 was employed. The generated future weather datasets have been then used as an input parameter in the dynamic energy performance simulations of the residential building unit with Energy Plus. For all examined weather datasets, the simulation results show a decrease of the heating energy use, an effect that is strongly counterbalanced by the rise of the cooling energy demand.

Development of a Dynamic Downscaling Method for Use in Short-Range Atmospheric Dispersion Modeling Near Nuclear Power Plants

Background: High-fidelity meteorological data is a prerequisite for the realistic simulation of atmospheric dispersion of radioactive materials near nuclear power plants (NPPs). However, many meteorological models frequently overestimate near-surface wind speeds, failing to represent local meteorological conditions near NPPs. This study presents a new high-resolution (approximately 1 km) meteorological downscaling method for modeling short-range (< 100 km) atmospheric dispersion of accidental NPP plumes.Materials and Methods: Six considerations from literature reviews have been suggested for a new dynamic downscaling method. The dynamic downscaling method is developed based on the Weather Research and Forecasting (WRF) model version 3.6.1, applying high-resolution land-use and topography data. In addition, a new subgrid-scale topographic drag parameterization has been implemented for a realistic representation of the atmospheric surface-layer momentum transfer. Finally, a year-long simulation for the Kori and Wolsong NPPs, located in southeastern coastal areas, has been made for 2016 and evaluated against operational surface meteorological measurements and the NPPs’ on-site weather stations.Results and Discussion: The new dynamic downscaling method can represent multiscale atmospheric motions from the synoptic to the boundary-layer scales and produce three-dimensional local meteorological fields near the NPPs with a 1.2 km grid resolution. Comparing the year-long simulation against the measurements showed a salient improvement in simulating near-surface wind fields by reducing the root mean square error of approximately 1 m/s. Furthermore, the improved wind field simulation led to a better agreement in the Eulerian estimate of the local atmospheric dispersion. The new subgrid-scale topographic drag parameterization was essential for improved performance, suggesting the importance of the subgrid-scale momentum interactions in the atmospheric surface layer.Conclusion: A new dynamic downscaling method has been developed to produce high-resolution local meteorological fields around the Kori and Wolsong NPPs, which can be used in short-range atmospheric dispersion modeling near the NPPs.

Downscaling Simulation of Groundwater Storage in the Beijing, Tianjin, and Hebei Regions of China Based on GRACE Data

Gravity Recovery and Climate Experiment (GRACE)-derived groundwater storage anomalies (GWSA) have been used to highlight groundwater depletion in regional aquifer systems worldwide. However, the use of GRACE products in smaller areas is limited owing to the coarse spatial resolution of the data product. This study utilized a dynamic downscaling method to improve the GWSA resolution from 1° to 0.05° by constructing a groundwater storage numerical model in the Beijing, Tianjin, and Hebei regions of China (BTH). The results indicate that: (1) the GRACE-derived and calculated GWSA had a good match with an average root mean squared error (RMSE) of 2.61 cm equivalent water height (EWH) and an average Nash–Sutcliffe efficiency coefficient (NSE) of 0.84 for the calibration period. (2) The hydraulic gradient coefficient and specific yield had the highest sensitivity, and transmissivity had the lowest sensitivity; however, different forcing data had no obvious influence on the GWSA. (3) The downscaled results not only exhibited time series variations that were consistent with those of the GRACE-derived solutions but also revealed a finer spatial heterogeneity of the GWSA along with increasing correlation coefficients between the GRACE-derived GWSA and the in situ measurements of groundwater levels by 0.06 and reducing the RMSE by 8.85%. (4) The downscaled results reflected the spatiotemporal change characteristics of groundwater storage in different hydrogeological units and administrative regions well. This study demonstrates the potential applications of the proposed downscaling method for both regional and local groundwater resource management.

Long-term wave energy potential estimation in the Aegean and Ionian seas using dynamic downscaling and wave modelling techniques.

The current work focuses on the use of dynamic downscaling methods based on 40 years ERA5 hindcast climatic data, for estimating the Wave energy potential for the Hellenic region. The main areas of interest are the Aegean and Ionian islands. The combination of local and reanalysis climatic data, has been used, as it is linking the mesoscale meteorology, with microclimatic weather phenomena. This way the driving geostrophic winds are effectively taken into account for the estimation of low altitude wind distribution. WRF is used to downscale the raw reanalysis ERA5 input data grid to a finer 3 × 3 Km results grid. This way, high computational accuracy can be achieved over the investigated regions, thus revealing finer wind scales phenomena. The downscaled climatic data are used as initial and boundary conditions for NOAA/NCEP's WAVEWATCH III to produce the required wave climate for the areas of interest over a finer grid. The computational results are compared with existing measurements and the significant wave height, wave period and direction distributions are used to assess in detail the potential wave energy distribution in specific areas of interest. The seasonal wave-climate variability and long-term periodicity are also investigated.

A simple hybrid statistical–dynamical downscaling method for emulating regional climate models over Western Europe. Evaluation, application, and role of added value?

A hybrid statistical dynamical downscaling method intended to emulate regional climate models is described and applied to Western Europe. The method is based on a constructed analogues algorithm, already used for statistical downscaling. For emulation, the statistical downscaling relationship is not derived from observations but from climate projections at low and high resolution. The hybrid approach therefore does not rely on the stationarity assumption inherent to conventional statistical downscaling. Within a perfect model framework, and using a large number of regional projections, the hybrid method is shown to reproduce climate change signals very well and to outperform a conventional statistical downscaling method also based on constructed analogues. The hybrid approach remains skillful even when applied to very low resolution climate data. In practice, two emulation modes exist. In the GCM/RCM mode, the downscaling relationship is built between a RCM and its forcing GCM. In the RCM/RCM mode, the relationship is built between a RCM and the same RCM after aggregation of its results to a low resolution grid. The large-scale climate change signal of the downscaled GCM is generally retained with the RCM/RCM mode, but not with the GCM /RCM mode. Additionally, the choice of the GCM/RCM pair used for learning leads to large differences in downscaling results at large scale (i.e. at low resolution) with the GCM /RCM mode, but not with the RCM/RCM mode. These results are explained by the differences that generally exist at large scale between projected changes by current RCMs and their forcing GCMs. Whether these differences are a testimony of a real added value of RCMs at large scale in the climate change context, or whether they have other causes, is therefore a crucial question.

Disentangling the Contribution of Moisture Source Change to Isotopic Proxy Signatures: Deuterium Tracing with WRF-Hydro-Iso-Tag and Application to Southern African Holocene Sediment Archives

Abstract It is well accepted that global circulation models equipped with stable water isotopologues help us better understand the relationships between atmospheric circulation changes and isotope records in paleoclimate archives. Still, isotope-enabled models do not disentangle the different processes affecting precipitation isotopic compositions. Furthermore, the relevance of this model-oriented approach relies on the realism of the modeled isotope results, which would support the interpretation of the proxy records in terms of modeled climate changes. To alleviate these limitations, the newly developed WRF-Hydro-iso-tag, a version of the isotope-enabled regional coupled model WRF-Hydro-iso enhanced with an isotope-tracing procedure, is presented. Physics-based WRF-Hydro-iso-tag ensembles are used to regionally downscale the isotope-enabled Community Earth System Model for southern Africa, for two 10-yr slices of mid-Holocene and preindustrial times. The isotope-tracing procedure is tailored to assess the origin of the hydrogen isotope deuterium contained in southern African precipitation, between the Atlantic and Indian Oceans. In comparison to the global model, WRF-Hydro-iso-tag simulates lower precipitation amounts with more regional details, as well as mid-Holocene-to-preindustrial changes in precipitation isotopic compositions that better match plant-wax deuterium records from two marine sediment cores off the Orange and Limpopo River basins. Linear relationships between mid-Holocene-to-preindustrial changes in temperature, precipitation amount, moisture source, and precipitation deuterium compositions are derived from the ensemble results. A deuterium enrichment in the Orange River-related sediment core may not be related to an aridification but rather indicate a summer circulation change enabling a larger contribution of more isotopically enriched moisture from the Atlantic Ocean. Significance Statement The knowledge of past climates is crucial for understanding our Earth system and apprehending future climate change. Plant materials in sediment archives contain atoms of hydrogen from past precipitation that allow paleoclimate reconstructions, using compositions of the hydrogen isotope deuterium. However, in the tropics, deuterium-depleted plant remains can either denote a wetter climate phase or a change in atmospheric circulation patterns with longer distances between ocean evaporation and land precipitation. This work provides an innovative dynamical downscaling method of global paleoclimate models to disentangle the effects of precipitation amount change and moisture source change on deuterium records, and ultimately to improve paleoclimate reconstructions. The interpretation of a deuterium enrichment in a marine sediment core as a marker for aridification is revised. The enrichment caused by an atmospheric circulation change bringing a larger amount of more isotopically enriched moisture flow from the Atlantic Ocean to southern African precipitation would be a more physically sound explanation.

Predicting soil conservation service in the Jinghe River Basin under climate change

As an essential regulating service, soil conservation service is the primary way to suppress soil erosion. Accurate assessment of soil conservation service can provide scientific support for regional ecological protection and is of great significance to regional environmental security. However, because of the lack of future high-resolution meteorological data, there are few researches on ecosystem services prediction, resulting in limited persuasion to guide ecological construction. Therefore, this study uses the WRF model to obtain a high-resolution meteorological dataset (0.05° × 0.05°, 2021–2035, daily) to drive the SWAT model in the Jinghe River basin. Through this method, we introduce the dynamical downscaling method into the research field of ecosystem services to predict the temporal-spatial characteristics of soil conservation service. The results indicate that: first, the dynamical downscaling results of the WRF model can reflect the magnitude, temporal fluctuation, and spatial distribution of regional precipitation and temperature. Second, multi-year average (2021–2035) soil conservation is 9.79 × 108 t (RCP4.5) and 10.98 × 108 t (RCP8.5), which are significantly higher than the 2000–2020 period (4.21 × 108 t). The inter-annual variation of soil conservation service is affected considerably by precipitation, with correlation coefficients of 0.80 (RCP4.5) and 0.81 (RCP8.5). Finally, the spatial distribution of soil conservation service under two scenarios is similar. The research results provide scientific support for ecological construction and scientific management, and the research ideas are also applicable to other ecosystem services prediction at the watershed scale.

Enhanced Understanding of Groundwater Storage Changes under the Influence of River Basin Governance Using GRACE Data and Downscaling Model

The low spatial resolution of Gravity Recovery and Climate Experiment (GRACE) data limits their application in practical groundwater resource management. To overcome this limitation, this study developed a dynamic downscaling method based on a model using groundwater storage anomaly (GWSA) data to study groundwater storage changes in an inland arid region. The groundwater storage model was calibrated using publicly accessible data at a spatial resolution of 1°. The constructed model had a satisfactory fitting effect in both the calibration and validation periods, with correlation coefficients over 0.60, in general, and a root mean square error of less than 1.00 cm equivalent water height (EWH). It was found that the hydraulic gradient coefficient was the most sensitive parameter, whereas the boundary condition had an obvious influence on the simulated GWSA compared to the different forcing data. The model was then refined at a higher resolution (0.05°) using driving data to obtain downscaled GWSA data. The downscaled results had a similar pattern to the GRACE-derived GWSA and reflected the spatial heterogeneity across the basin scale and subregion scales. The downscaled GWSA shows that the groundwater storage had an overall downward trend during the period from 2003 to 2019 and the annual decline rates ranged from 0.22 to 0.32 cm/year in four subregions. A four-month time lag between the field-observed and downscaled GWSA was observed downstream of the study area. This study provides an applicable method for assessing groundwater storage changes for groundwater management at the local scale.

Advances in the carbon sink potential of terrestrial ecosystems in China

<p indent="0mm">The “carbon peak and carbon neutrality” concept has become a major demand for global climate governance and ecological civilization construction in China. Understanding the potential of terrestrial carbon sequestration and its response to climate change and human activities is very important for accurately predicting the impacts of future climate change on terrestrial ecosystems and is the basic premise for human beings to cope with climate change. Based on the perspective of terrestrial ecosystem carbon storage and carbon sinks, this paper summarizes the carbon storage of forest, shrub, grassland, farmland and wetland ecosystems of China, thus assessing the carbon sink potential of the Chinese terrestrial ecosystem and its response to different climate change scenarios and human activities. The evaluation results varied greatly due to different research methods and data. The average biomass carbon density of China’s forests and grasslands was approximately 55.7 and 4.8 Mg C ha⁻¹, respectively, which were lower than the average values of the global forests (94.2 Mg C ha⁻¹) and grasslands (7.2 Mg C ha⁻¹), mainly due to larger areas of young forests and extensive grazing. The average biomass carbon density of the wetland was approximately 4.1 Mg C ha⁻¹, which varied greatly for different types of wetland vegetation, and the highest value was located in coastal wetland vegetation (9.6 Mg C ha⁻¹). The biomass carbon density of shrub and farmland was approximately 4.8–9.6 and 3.06–7.1 Mg C ha⁻¹, respectively. The carbon sink potential of China’s main terrestrial ecosystems (forest, shrub, grassland and farmland) estimated by different methods varied greatly. According to the eighth (2009–2013) and ninth (2014–2018) inventory data of China’s forest resources and other inventory data, the latest result of the carbon storage increase rate of China’s terrestrial ecosystem was estimated to be approximately <sc>0.28 PG C a⁻¹.</sc> Ecological engineers should greatly promote the carbon sink potential of terrestrial ecosystems. The terrestrial ecosystem carbon sink of six major national ecological engineering projects in China reached approximately 132 Tg C a⁻¹, of which more than half (74 Tg C a⁻¹, 56%) was attributed to the implementation of ecological engineering projects. The carbon sink increase was approximately 14.7 and 14 Tg C a⁻¹ from the conversion of farmland to grassland engineering area and natural forest protection engineering area, respectively, and approximately 7.0−12 Tg C a⁻¹ in each of the other five engineering areas. Thus, it is necessary to constantly improve the assessment methods of terrestrial ecosystem carbon reserves and carbon sinks and constantly update the assessment results based on the latest data. The shortcomings of the existing studies are put forward in this paper. (1) Integrated research on the carbon sink function of China’s terrestrial ecosystems in different periods and different emission scenarios in the future is insufficient, especially on the sink potential of different kinds of major ecological projects in the same region. (2) Impact research on nitrogen deposition and its interaction with climate change and atmospheric CO₂ concentration on the carbon sink potential of China’s terrestrial ecosystems is still insufficient, especially regarding the carbon sink potential of major national ecological projects. (3) Research on the climate change risk of carbon sequestration in China’s terrestrial ecosystems has not been conducted. Finally, this research identifies research activities on carbon storage and carbon sinks in China’s terrestrial ecosystems in the future, including carbon sink potential and a risk assessment of major national ecological projects, the simulation technology of the regional terrestrial ecosystem carbon budget with high spatial heterogeneity, and the dynamic downscaling method and correction technology of meteorological data under different climate change scenarios, to provide scientific support for the systematic management optimization scheme of increasing sinks in China’s terrestrial ecosystem and to serve the strategy of national carbon neutrality.