Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought ...monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions.
Drought affects a region’s economy intensively and its severity is based on the level of infrastructure present in the affected region. Therefore, it is important not only to reflect on the ...conventional environmental properties of drought, but also on the infrastructure of the target region for adequate assessment and mitigation. Various drought indices are available to interpret the distinctive meteorological, agricultural, and hydrological characteristics of droughts. However, these drought indices do not consider the effective assessment of damage of drought impact. In this study, we evaluated the applicability of satellite-based drought indices over North Korea and South Korea, which have substantially different agricultural infrastructure systems to understand their characteristics. We compared satellite-based drought indices to in situ-based drought indices, standardized precipitation index (SPI), and rice yield over the Korean Peninsula. Moderate resolution imaging spectroradiometer (MODIS), tropical rainfall measuring mission (TRMM), and global land data assimilation system (GLDAS) data from 2001 to 2018 were used to calculate drought indices. The correlations of the indices in terms of monitoring meteorological and agricultural droughts in rice showed opposite correlation patterns between the two countries. The difference in the prevailing agricultural systems including irrigation resulted in different impacts of drought. Vegetation condition index (VCI) and evaporative stress index (ESI) are best suited to assess agricultural drought under well-irrigated regions as in South Korea. In contrast, most of the drought indices except for temperature condition index (TCI) are suitable for regions with poor agricultural infrastructure as in North Korea.
This study investigates the physical mechanisms that contributed to the 2016 Eurasian heat wave during boreal summer season (July-August, JA), characterized by much higher than normal temperatures ...over eastern Europe, East Asia, and the Kamchatka Peninsula. It is found that the 2016 JA mean surface air temperature, upper-tropospheric height, and soil moisture anomalies are characterized by a tri-pole pattern over the Eurasia continent and a wave train-like structure not dissimilar to recent (1980-2016) trends in those quantities. A series of forecast experiments designed to isolate the impacts of the land, ocean, and sea ice conditions on the development of the heat wave is carried out with the Global Seasonal Forecast System version 5. The results suggest that the tri-pole blocking pattern over Eurasia, which appears to be instrumental in the development of the 2016 summer heat wave, can be viewed as an expression of the recent trends, amplified by record-breaking oceanic warming and internal land-atmosphere interactions.
This study uses a global land–atmosphere coupled model, the land–atmosphere component of the Global Seasonal Forecast System version 5, to quantify the degree to which soil moisture initialization ...could potentially enhance boreal summer surface air temperature forecast skill. Two sets of hindcast experiments are performed by prescribing the observed sea surface temperature as the boundary condition for a 15-year period (1996–2010). In one set of the hindcast experiments (noINIT), the initial soil moisture conditions are randomly taken from a long-term simulation. In the other set (INIT), the initial soil moisture conditions are taken from an observation-driven offline Land Surface Model (LSM) simulation. The soil moisture conditions from the offline LSM simulation are calibrated using the forecast model statistics to minimize the inconsistency between the LSM and the land–atmosphere coupled model in their mean and variability. Results show a higher boreal summer surface air temperature prediction skill in INIT than in noINIT, demonstrating the potential benefit from an accurate soil moisture initialization. The forecast skill enhancement appears especially in the areas in which the evaporative fraction—the ratio of surface latent heat flux to net surface incoming radiation—is sensitive to soil moisture amount. These areas lie in the transitional regime between humid and arid climates. Examination of the extreme 2003 European and 2010 Russian heat wave events reveal that the regionally anomalous soil moisture conditions during the events played an important role in maintaining the stationary circulation anomalies, especially those near the surface.
This study investigates the predictability of the 2018 Northern Europe heatwave using the GloSea5 forecast model from the perspective of land-atmosphere interactions. We focus on an inverse ...relationship wherein soil drying leads to increased temperatures and the model's ability to simulate this hypersensitivity in the soil moisture-temperature coupling on the dry side of a breakpoint defined as the soil moisture threshold below which land feedbacks nonlinearly amplify extreme heat. When evaluating forecast model performance in predicting this heatwave, we compare deterministic forecast scores (Hit Rate (HR) and True Skill Score (TSS)) for whether model Surface Soil Moisture (SSM) falls within the hypersensitive regime. GloSea5 exhibits enhanced prediction skill for the extreme heat event when the modelled soil moisture is within the hypersensitive regime. To understand the skill of the heatwave forecast for hit and missed cases of capturing SSM below the breakpoint, we first evaluate the climatological model performance for the water- and energy-limited processes, and then perform a comparison classified by whether SSM verifies on the dry side of the wilting point. The composite analysis demonstrates that the reproducibility of the breakpoint is tied to an improvement in climatological land coupling processes, mainly for classification in the water-limited coupling regime. Therefore, the results suggest that the process-based connection between soil moisture and temperature is a potential source for improving heatwave forecasts on subseasonal to seasonal (S2S) time scales.
Abstract
Given their conditions to reside in and intensify longer over warm oceans, tropical cyclones (TCs) in the western North Pacific (WNP) present a stronger lifetime maximum intensity during El ...Niño than during La Niña. By using observational data, we found that the anomalously cool sea surface temperature (SST)s in the basin act as effective barriers against intense TCs approaching East Asia during El Niño, weakening their destructiveness at landfall. Based on our high-resolution pseudo-global-warming simulations, the basin-wide 2K SST warming within the WNP basin can, however, shatter this cool SST barrier, exposing East Asia to more destructive TCs during El Niño, compared to those during La Niña. Considering that the 2K warmer WNP will likely occur in the mid-21st century under a high emission scenario and in the late 21st century under a moderate emission scenario, our findings support that more aggressive efforts of global warming mitigation are needed.
Abstract
Models have historically been the source of global soil moisture (SM) analyses and estimates of land-atmosphere coupling, even though they are usually calibrated and validated only locally. ...Satellite-based analyses have grown in fidelity and duration, offering an independent observationally-based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land-atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physical-based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with
in situ
observations.
When the original and time-filtered SM products are evaluated against ground-based SM measurements over the conterminous U.S., Europe, and Australia, results show the filtered SM has significantly improved subseasonal variability. The skill of the time-filtered SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM), and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. Time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically-based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.
High temperature extremes accompanied by drought have led to serious ramifications for environmental and socio‐economic systems. Thus, improving the predictability of heat‐wave events is a high ...priority. One key to achieving this is to better understand land‐atmosphere interactions. Recent studies have documented a hypersensitive regime in the soil moisture‐temperature relationship: when soil dries below a critical low threshold, called the soil moisture breakpoint, air temperatures increase at a greater rate as soil moisture declines. Whether such a hypersensitive regime is rooted in land surface processes and whether this soil moisture breakpoint corresponds to a known plant critical value, the permanent wilting point (WP), below which latent heat flux almost ceases, remains unclear. In this study, we explore the mechanisms linking low soil moisture to high air temperatures. From in situ observations, we confirm that the hypersensitive regime acts throughout the chain of energy processes from land to atmosphere. A simple energy‐balance model indicates that the hypersensitive regime occurs when there is a dramatic drop in evaporative cooling, which happens when soil moisture dries toward the permanent WP, suggesting that the soil moisture breakpoint is slightly above the permanent WP. Precisely how a model represents the relationship between evapotranspiration and soil moisture is found to be essential to describe the occurrence of the hypersensitive regime. Thus, we advocate that weather and climate models should ensure a realistic representation of land‐atmosphere interactions to obtain reliable forecasts of extremes and climate projections, aiding the assessment of heatwave vulnerability and adaptation.
Plain Language Summary
Hot temperature extremes combined with droughts have caused significant problems for the environment and economies. Improving prediction of heat‐wave events is of utmost importance. This can be achieved by a better understanding of how land conditions affect near surface atmosphere and vice versa. Recent evidences have shown that when the soil becomes very dry and below a certain threshold, even a slight decrease in soil moisture yields a substantial increase in air temperature. However, the behind mechanism remains unclear. In this study, we validate that hypersensitive regimes indeed result from energy transmission from land to atmosphere by using observations. Subsequently, we built a simple model to explore how air temperature correlates to land wetness conditions. Our model indicates that hypersensitive regime occurs when there is a dramatic drop in evaporation when soil moisture dries to the permanent wilting point, below which water is no longer drawn from the soil by plant roots. The diminished evaporation significantly curtails the cooling effect on the atmosphere. Notably, the model’s representation of evaporation behavior fundamentally governs the occurrence of hypersensitive regimes. To achieve reliable forecasts of climate extremes and projections, a realistic depiction of land‐atmosphere interactions is indispensable.
Key Points
Hypersensitive regime acts throughout the chain of energy processes from land to atmosphere
Hypersensitive regime occurs when soil moisture dries to the permanent wilting point
Model’s representation of evapotranspiration fundamentally governs the occurrence of hypersensitive regimes
A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble ...Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system.
•Active and passive microwave soil moisture retrievals were assimilated into JULES LSM.•The Local Ensemble Transform Kalman Filter is used in the soil moisture assimilation.•SMAP retrievals are more beneficial than ASCAT for soil moisture skill improvement.•Assimilation metrics are proposed to evaluate results from assimilation experiments.•Assimilated soil moisture estimates may further improve drought monitoring systems.
•A machine learning-based ensemble model was developed to quantify soil moisture (SM).•The ensemble model was effective in modeling dynamic SM over the CONUS.•The model showed robust performance over ...dense vegetation and complex topography.•The ensemble model yielded better performance than existing SM data.•Triple collocation analysis revealed the spatially robust performance of the model.
Three widely used primary soil moisture (SM) data sources, namely, in-situ measurements, satellite observations, and land surface models (LSM), possess different characteristics. This study combined three SM data sources using machine learning (ML): random forest, artificial neural networks, and support vector regression, and simple averaging ensemble approaches to produce improved daily SM data over the contiguous United States (CONUS). For each ML model, three schemes were tested using different independent variables, namely, satellite-derived, LSM-derived, and both. Triple collocation analysis (TCA) was adopted to address the scale mismatch problem between in-situ and coarse gridded SM data. The proposed approach was evaluated using the International Soil Moisture Network (ISMN), Soil Moisture Active Passive Core Validation Sites (SMAP CVS), and TCA. In the ISMN-based evaluation, the proposed ML-based ensemble generally produced better evaluation metrics and showed robust skills over topographically complex and densely vegetated regions where existing SM products showed poor skills. The SMAP CVS-based evaluation demonstrated that the ML ensemble approach yielded a better performance than the existing SM datasets, resulting in a correlation coefficient of 0.78, unbiased root mean squared difference of 0.035 m3/m3, and bias of 0.006 m3/m3. In addition, the TCA results additionally confirmed that the ML-based ensemble had better spatiotemporal quality than the other SM products. The data-driven approach proposed in this study has three major novelties: (1) the proposed ML-based method synergistically merges various data sources to improve SM; (2) the performance of the proposed ML-based SM was robust to topography and vegetation; and (3) the average ensemble of three ML models additionally improves performances. The SM time-series data generated by the proposed approach are expectedly suitable variables for environmental and climate applications over CONUS. The research findings suggest that ML algorithms can be effectively used for modeling dynamic soil moisture.
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