Abstract. A multitude of physical and biological processes on which ecosystems and human societies depend are governed by the climate, and
understanding how these processes are altered by climate change is central
to mitigation efforts. We developed a set of climate-related variables at
as yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We downscaled time series of near-surface relative
humidity (hurs) and cloud area fraction (clt) under the consideration of orography and wind as well as near-surface wind speed (sfcWind) using the delta-change
method. Combining these grids with mechanistically downscaled information on
temperature, precipitation, and solar radiation, we then calculated vapor pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential
evapotranspiration (pet), the climate moisture index (cmi), and site water balance (swb) at a monthly temporal and 30 arcsec spatial resolution globally from 1980 until 2018 (time-series variables). At the same spatial resolution, we
further estimated climatological normals of frost change frequency (fcf), snow
cover days (scd), potential net primary productivity (npp), growing degree days
(gdd), and growing season characteristics for the periods 1981–2010, 2011–2040,
2041–2070, and 2071–2100, considering three shared socioeconomic pathways
(SSP126, SSP370, SSP585) and five Earth system models (projected variables).
Time-series variables showed high accuracy when validated against
observations from meteorological stations and when compared to alternative products. Projected variables were also highly correlated with observations,
although some variables showed notable biases, e.g., snow cover days.
Together, the CHELSA-BIOCLIM+ dataset presented here (https://doi.org/10.16904/envidat.332, Brun et al., 2022) allows improvement to
our understanding of patterns and processes that are governed by climate,
including the impact of recent and future climate changes on the world's ecosystems and the associated services on societies.


/pdf/global-climate-related-predictors-at-kilometer-resolution-3ai943v9.pdf

Global climate-related predictors at kilometer resolution for the past and future

Identification of future meteorological drought hotspots over Indian region: A study based on NEX-GDDP data

Compound heat and moisture extreme impacts on global crop yields under climate change

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.

/pdf/improving-spatial-disaggregation-of-crop-yield-by-38693ne1.pdf

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Internal variability, multiple emission scenarios, and different model responses to anthropogenic forcing are ultimately behind a wide range of uncertainties that arise in climate change projections. Model weighting approaches are generally used to reduce the uncertainty related to the choice of the climate model. This study compares three multi-model combination approaches: a simple arithmetic mean and two recently developed weighting-based alternatives. One method takes into account models’ performance only and the other accounts for models’ performance and independence. The effect of these three multi-model approaches is assessed for projected changes of mean precipitation and temperature as well as four extreme indices over northern Morocco. We analyze different widely used high-resolution ensembles issued from statistical (NEXGDDP) and dynamical (Euro-CORDEX and bias-adjusted Euro-CORDEX) downscaling. For the latter, we also investigate the potential added value that bias adjustment may have over the raw dynamical simulations. Results show that model weighting can significantly reduce the spread of the future projections increasing their reliability. Nearly all model ensembles project a significant warming over the studied region (more intense inland than near the coasts), together with longer and more severe dry periods. In most cases, the different weighting methods lead to almost identical spatial patterns of climate change, indicating that the uncertainty due to the choice of multi-model combination strategy is nearly negligible.

/pdf/changes-in-mean-and-extreme-temperature-and-precipitation-runxcip9ib.pdf

Changes in mean and extreme temperature and precipitation events from different weighted multi-model ensembles over the northern half of Morocco

Future climate change significantly alters interannual wheat yield variability over half of harvested areas

Crop rotations, the farming practice of growing crops in sequential seasons, occupy a core position in agriculture management, showing a key influence on food security and agro-ecosystem sustainability. Despite the improvement in accuracy of identifying mono-agricultural crop distribution, crop rotation patterns remain poorly mapped. In this study, a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) architecture, namely crop rotation mapping (CRM), were proposed to synergize the synthetic aperture radar (SAR) and optical time series in a rotational mapping task. The proposed end-to-end architecture had reasonable accuracies (i.e., accuracy > 0.85) in mapping crop rotation, which outperformed other state-of-the-art non-deep or deep-learning solutions. For some confusing rotation types, such as fallow-single rice and crayfish-single rice, CRM showed substantial improvements from traditional methods. Furthermore, the deeply synergistic SAR-optical, time-series data, with a corresponding attention mechanism, were effective in extracting crop rotation features, with an overall gain of accuracy of four points compared with ablation models. Therefore, our proposed method added wisdom to dynamic crop rotation mapping and yields important information for the agro-ecosystem management of the study area.

Shuo Chen

Papers

Future climate change significantly alters interannual wheat yield variability over half of harvested areas

Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series

Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 over the Qinghai-Tibet Plateau.

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Reducing livestock snow disaster risk in the Qinghai–Tibetan Plateau due to warming and socioeconomic development