Global warming increases the occurrence probability of hot extremes, and improving the predictability of such events is thus becoming of critical importance. Hot extremes have been shown to be induced by surface moisture deficits in some regions. In this study, we assess whether such a relationship holds at the global scale. We find that wide areas of the world display a strong relationship between the number of hot days in the regions’ hottest month and preceding precipitation deficits. The occurrence probability of an above-average number of hot days is over 70% after precipitation deficits in most parts of South America as well as the Iberian Peninsula and Eastern Australia, and over 60% in most of North America and Eastern Europe, while it is below 30–40% after wet conditions in these regions. Using quantile regression analyses, we show that the impact of precipitation deficits on the number of hot days is asymmetric, i.e. extreme high numbers of hot days are most strongly influenced. This relationship also applies to the 2011 extreme event in Texas. These findings suggest that effects of soil moisture-temperature coupling are geographically more widespread than commonly assumed.

Hot days induced by precipitation deficits at the global scale

In situ soil moisture data from more than 200 stations located in Africa, Australia, Europe and the United States are used to determine the reliability of three soil moisture products, one analysis from the ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction system (SM-DAS-2) and two remotely sensed soil moisture products, namely ASCAT (Advanced scatterometer) and SMOS (Soil Moisture Ocean Salinity). SM-DAS-2 is produced offline at ECMWF and relies on an advanced surface data assimilation system (Extended Kalman Filter) used to optimally combine conventional observations with satellite measurements. ASCAT remotely sensed surface soil moisture is provided in near real time by EUMETSAT. At ECMWF, ASCAT is used for soil moisture analyses in SM-DAS-2, also. Finally the SMOS remotely sensed soil moisture data level two product developed at CESBIO is used. Evaluation of the times series as well as of the anomaly values, shows good performances of the three products to capture surface soil moisture annual cycle and short term variability. Correlations with in situ data are very satisfactory over most of the investigated sites located in contrasted biomes and climate conditions with averaged values of 0.70 for SM-DAS-2, 0.53 for ASCAT and 0.54 for SMOS. Although radio frequency interference disturbs the natural microwave emission of the Earth observed by SMOS in several parts of the world, hence the soil moisture retrieval, performances of SMOS over Australia are very encouraging.

Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors - including mean climate as well as climate extremes - explain 20%-49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%-43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.

https://iopscience.iop.org/article/10.1088/1748-9326/ab154b/pdf

The effects of climate extremes on global agricultural yields

. The European Space Agency's Climate Change Initiative
for Soil Moisture (ESA CCI SM) merging algorithm generates consistent
quality-controlled long-term (1978–2018) climate data records for soil
moisture, which serves thousands of scientists and data users worldwide. It
harmonises and merges soil moisture retrievals from multiple satellites into
(i) an active-microwave-based-only product, (ii) a passive-microwave-based-only product and (iii) a combined
active–passive product, which are sampled to daily global images on a
0.25 ∘ regular grid. Since its first release in 2012 the algorithm has
undergone substantial improvements which have so far not been thoroughly
reported in the scientific literature. This paper fills this gap by reviewing
and discussing the science behind the three major ESA CCI SM merging
algorithms, versions 2 ( https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c ;
 Wagner et al. ,  2018 ), 3 ( https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c ;
 Dorigo et al. ,  2018 ) and 4 ( https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e ;
 Dorigo et al. ,  2019 ), and provides an outlook on the expected improvements
planned for the next algorithm, version 5.

/pdf/evolution-of-the-esa-cci-soil-moisture-climate-data-records-4mgtp4va1t.pdf

Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology

. New precipitation ( P ) datasets are released regularly, following
innovations in weather forecasting models, satellite retrieval methods, and
multi-source merging techniques. Using the conterminous US as a case study,
we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain
insight into the merit of these innovations. The evaluation was performed at
a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency
(KGE), a performance metric combining correlation, bias, and variability. As
a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P 
dataset. Among the three KGE components, the P datasets performed worst
overall in terms of correlation (related to event identification). In terms
of improving KGE scores for these datasets, improved P totals (affecting
the bias score) and improved distribution of P intensity (affecting the
variability score) are of secondary importance. Among the 11 gauge-corrected
 P datasets, the best overall performance was obtained by MSWEP V2.2,
underscoring the importance of applying daily gauge corrections and
accounting for gauge reporting times. Several uncorrected P datasets
outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the
best performance was obtained by the ERA5-HRES fourth-generation reanalysis,
reflecting the significant advances in earth system modeling during the last
decade. The (re)analyses generally performed better in winter than in summer,
while the opposite was the case for the satellite-based datasets. IMERGHH V05
performed substantially better than TMPA-3B42RT V7, attributable to the many
improvements implemented in the IMERG satellite P retrieval algorithm.
IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms,
while the opposite was observed in regions of complex terrain. The ERA5-EDA
ensemble average exhibited higher correlations than the ERA5-HRES
deterministic run, highlighting the value of ensemble modeling. The WRF
regional convection-permitting climate model showed considerably more
accurate P totals over the mountainous west and performed best among the
uncorrected datasets in terms of variability, suggesting there is merit in
using high-resolution models to obtain climatological P statistics. Our
findings provide some guidance to choose the most suitable P dataset for a
particular application.

/pdf/daily-evaluation-of-26-precipitation-datasets-using-stage-iv-4dfcfx313i.pdf

Pascal Lecomte

Papers

ESA CCI Soil Moisture for improved Earth system understanding : State-of-the art and future directions

The ESA Climate Change Initiative (CCI): A European contribution to the generation of the Global Climate Observing System