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Wildfires are disturbances that occur in ecosystems, both naturally and derived from anthropogenic factors, often caused by extreme meteorological conditions, and have recurrently destructive impacts on forests throughout the world. The complex nature of the interactions between wildfires, their dynamics, and human interference from a climate change perspective has motivated a growing number of researchers to address this topic. The fire weather index (FWI) has been extensively used to analyze the link between meteorological fire danger and its local to regional characteristics contributing to the severity of these events, as well as real-time operational monitoring at national and international levels. Recently, a new improved fire danger index that includes the effect of atmospheric instability has been developed, the so-called FWIe. The presence of instability in the atmosphere may be a boost to more energetic wildfires, such as the June 2017 extreme event in central Portugal, making it an important asset in risk monitoring and management. Here, a comprehensive examination of future fire risk on the Iberian Peninsula was performed. Additionally, a comparative analysis between FWI and FWIe was pursued in the context of climate change. We computed both FWI and FWIe using a multi-model ensemble composed of 13 Euro-CORDEX Regional Climate Model (RCM) simulations forced by different global climate models. The historical period (1971–2000) and three projected periods of 30 years (2011–2040, 2041–2070, and 2071–2 100), under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were considered. When assessing modelled FWI and FWIe, results show that summer values tend to substantially increase in the future when assuming the historical period as the benchmark, with an expected extension of the danger period to June and, in a lower magnitude, to September. The north-western region of Iberia, including the north of Portugal and the north-western-to-central Spain are the regions with larger increases in danger in the future, which may be critical since these are the regions with more fire-prone vegetation. This work also points to large differences in fire danger projections among scenarios, calling for a distinct set of adaptation needs that should be timely prepared by stakeholders and authorities.

Abstract Previous studies have primarily focused on the influence of temperature and precipitation on phenology. It is unclear if the easily ignored climate factors with drivers of vegetation growth can effect on vegetation phenology. In this research, we conducted an analysis of the start (SOS) and end (EOS) of the growing seasons in the northern region of China above 30°N from 1982 to 2014, focusing on two-season vegetation phenology. We examined the response of vegetation phenology of different vegetation types to preseason climatic factors, including relative humidity (RH), shortwave radiation (SR), maximum temperature (Tmax), and minimum temperature (Tmin). Our findings reveal that the optimal preseason influencing vegetation phenology length fell within the range of 0–60 days in most areas. Specifically, SOS exhibited a significant negative correlation with Tmax and Tmin in 44.15% and 42.25% of the areas, respectively, while EOS displayed a significant negative correlation with SR in 49.03% of the areas. Additionally, we identified that RH emerged as the dominant climatic factor influencing the phenology of savanna (SA), whereas temperature strongly controlled the SOS of deciduous needleleaf forest (DNF) and deciduous broadleaf forest (DBF). Meanwhile, the EOS of DNF was primarily influenced by Tmax. In conclusion, this study provides valuable insights into how various vegetation types adapt to climate change, offering a scientific basis for implementing effective vegetation adaptation measures.

Abstract The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs’ simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO2 concentration under high-emission scenarios in the mid- and late-future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China.

ABSTRACTStandardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), traditionally derived at a monthly scale, are widely used drought indices. To overcome temporal-resolution limitations, we have previously developed and published a well-validated daily SPI/SPEI in situ dataset. Although having a high temporal resolution, this in situ dataset presents low spatial resolution due to the scarcity of stations. Therefore, based on the China Meteorological Forcing Dataset, which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset, we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over China. It spans from 1979 to 2018, with a spatial resolution of 0.1° × 0.1°, a temporal resolution of 1-day, and the timescales of 30-, 90-, and 360-days. Results show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/SPEI. The correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest, with linear correlation, Nash-Sutcliffe coefficient, and normalized root mean square error of 0.98, 0.97, and 0.04, respectively. The daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/SPEI. Our improved SPI/SPEI shows high accuracy and credibility, presenting enhanced results when compared to the monthly SPI/SPEI. The total data volume is up to 150 GB, compressed to 91 GB in Network Common Data Form (NetCDF). It can be available from Figshare (https://doi.org/10.6084/m9.figshare.c.5823533) and ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103).

Abstract Due to global warming, drought events have become more frequent, which resulted in aggravated crop failures, food shortage, larger and more energetic wildfires, and have seriously affected socio-economic development and agricultural production. In this study, a global long-term (1981–2021), high-resolution (4 km) improved vegetation health index (VHI) dataset integrating climate, vegetation and soil moisture was developed. Based on drought records from the Emergency Event Database, we compared the detection efficiency of the VHI before and after its improvement in the occurrence and scope of observed drought events. The global drought detection efficiency of the improved high-resolution VHI dataset reached values as high as 85%, which is 14% higher than the original VHI dataset. The improved VHI dataset was also more sensitive to mild droughts and more accurate regarding the extent of droughts. This improved dataset can play an important role in long-term drought monitoring but also has the potential to assess the impact of drought on the agricultural, forestry, ecological and environmental sectors.

Abstract Climate change is expected to have impacts on the balance of global food trade networks and food security. Thus, seasonal forecasts of precipitation and temperature are an essential tool for stakeholders to make timely choices regarding the strategies required to maximize their expected cereal yield outcomes. The availability of state-of-the-art seasonal forecasts such as the European Centre for Medium-Range Weather Forecasts (ECMWF) system 5 (SEAS5) may be an asset to help decision making. However, uncertainties and reduced skill may hamper the use of seasonal forecasts in several applications. Hence, in this work, we aim to understand the added value of such dynamical forecasts when compared to persistent anomalies of climate conditions used to predict the production of wheat and barley yields. With that in mind, empirical models relating annual wheat and barley yields in Spain to monthly values of precipitation and temperature are developed by taking advantage of ECMWF ERA5 reanalysis. Then, dynamical and persistence forecasts are issued at different lead times, and the skill of the subsequent forecasted yield is verified through probabilistic metrics. The results presented in this study demonstrate two different outcomes: (1) wheat and barley yield anomaly forecasts (dynamical and persistent) start to gain skill later in the season (typically from April onwards); and (2) the added value of using the SEAS5 forecast as an alternative to persistence ranges from 6 to 16%, with better results in the southern Spanish regions.

Climate indices are developed to determine climate impacts on different socioeconomic sectors, providing a comprehensive communication of complex information arising from climate change assessments. These may be used by decision-makers to properly and timely implement climate change adaptation measures in different sectors of human activity, such as agriculture and crop selection, forest, and coastal management, among others. Here, we present a comprehensive analysis of climate indices estimated for Portugal, known to be in a climate change hotspot. A multi-variable 13-member ensemble of EURO-CORDEX Regional Climate Model simulations is used to assess future climate change projections of climate indices, exploring three future scenarios until 2100, and considering three different emission scenarios, namely the RCP2.6, RCP4.5 and RCP8.5. Aligned with warming and drying projected conditions, an increase in the number of summer days to very hot days is expected to become more frequent and intense, with more impact over interior regions. Tropical nights are projected to become more common, affecting the thermal comfort conditions and threatening future human health. Although the future projections show an overall reduction in the number of wet days, the amount of precipitation during short-time wet periods will increase leading to an intensification of moderate/heavy rainfall. These results corroborate that Portugal is in a climate change hotspot, calling for efficient policymaking by the relevant authorities. Indeed, such projections call for an urgent planning and development of adaptation measures to safeguard critical sectors of the Portuguese society, such as agriculture, forests, coastal management, among others.

Climate projections are a powerful tool that can help decision makers to timely prepare adaptation policies, which may then be efficiently implemented. In this study, a comprehensive analysis of how climate change may affect Portugal (located in a climate change hotspot) is conducted, providing the foundations to the first National Roadmap for Adaptation 2100. A multi-variable ensemble was built and tested, being the baseline for assessing future projections for three different emission scenarios (RCP2.6, RCP4.5, and RCP8.5) throughout the 21st century, accounting for the model’s ability to simulate a set of variables. A warmer and drier future climate is projected for the mainland, being more severe in the interior regions. Even for the optimistic RCP2.6 scenario, results indicate increases in temperature between 1 and 2 °C in comparison with the historical period and can surpass 6 °C in some regions for RCP8.5. Extreme hot events will be more frequent and severe, with maximum temperatures above 45 °C being more common throughout the 21st century. A decline in precipitation is expected mostly in summer and intermediate seasons, with losses above 40 % for the end-of-century. However, an intensification of heavy short-term rainfall events is projected to increase in northern regions. A rising of maximum wind gusts is also expected in these regions. Such projections call for an urgent planning and adaptation measures to safeguard critical sectors of the Portuguese society. Finally, the information here produced and examined constitutes a key example of climate services for mitigation and adaptation in Portugal.

Due to the complex coupling between drought and climatic factors, the future drought conditions that might occur under climate change is still unclear. In this research, we used the daily SPEI algorithm to project global drought conditions during 2016–2100 based on the data from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We also employed partial correlation analysis to explore the influence of climate factors on drought. Our analyses show the following: (1) Drought conditions projected by CMIP6 under different models are similar; however, they can vary widely across regions. (2) According to the MK trend test, drought conditions in most regions around the world are expected to become increasingly severe in the future, and this trend is significant. (3) Based on the results of the partial correlation analysis results, it is understood that drought events in most regions worldwide are primarily driven by precipitation. This study contributes to the discussion of projecting future drought conditions and expands the application by utilizing the state−of−the−art CMIP6 climate models and scenarios.Highlight

Abstract The impact of climate change on wheat and barley yields in two regions of the Iberian Peninsula is here examined. Regression models are developed by using EURO-CORDEX regional climate model (RCM) simulations, forced by ERA-Interim, with monthly maximum and minimum air temperatures and monthly accumulated precipitation as predictors. Additionally, RCM simulations forced by different global climate models for the historical period (1972–2000) and mid-of-century (2042–2070; under the two emission scenarios RCP4.5 and RCP8.5) are analysed. Results point to different regional responses of wheat and barley. In the southernmost regions, results indicate that the main yield driver is spring maximum temperature, while further north a larger dependence on spring precipitation and early winter maximum temperature is observed. Climate change seems to induce severe yield losses in the southern region, mainly due to an increase in spring maximum temperature. On the contrary, a yield increase is projected in the northern regions, with the main driver being early winter warming that stimulates earlier growth. These results warn on the need to implement sustainable agriculture policies, and on the necessity of regional adaptation strategies.

The Fire Weather Index (FWI) is used to assess meteorological fire danger worldwide. It has been argued that it lacks an atmospheric instability term. A new enhanced FWI (FWIe) was recently developed incorporating atmospheric instability in the form of the Continuous Haines Index (CHI). Here, the first climatological and evolution analysis of these indexes was performed using ERA5 data for the 1980–2020 period. There was a prevalence of higher values over central Iberia; these were heavily modulated by the climate types, topography, and land cover. Southwest and east Iberia suffered the greatest decadal increases in all three indexes. Relating both indexes to occurrences detected by satellite, through fire radiative power (FRP), showed that FWIe provided an improved meteorological fire danger assessment in higher-risk conditions. This showed that greater-risk observations were more prone to be affected by atmospheric instability than lower-danger observations. Case studies for the 2017 central Portugal and 2003 and 2018 Monchique wildfires were additionally conducted to verify these conclusions. This work points to the usefulness of FWIe when/where atmospheric instability may play a critical role in the development of wildfires, which may contribute to a more focused deployment of suppression mechanisms by the authorities.

Drought is one of the most complex and harmful natural disasters. A study on the temporal and spatial patterns and the evolution of drought can provide a scientific basis for predicting drought occurrences. Based on a multi-source dataset, we select a suitable control drought indicator for improving the vegetation health index (VHI), optimize its algorithm through Pearson correlation analysis, and compare the VHI performance before and after the improvement for various vegetation types. Results show that (1) the self-calibrated Palmer drought severity index is more suitable than the standardized precipitation evapotranspiration index for improving the VHI; (2) the contribution of the thermal condition index to the VHI in most parts of the world is higher than that of the vegetation condition index; (3) the enhanced VHI significantly improves the detection of vegetation drought; and (4) vegetation drought events occurring in high latitudes tend to worsen, and the response of different vegetation types to drought is significantly different. Our research presents a step forward in improving the effectiveness of the VHI in detecting vegetation drought and thus its application prospects. Furthermore, the response characteristics of various vegetation types to drought are identified, deepening our understanding of vegetation drought, which may help decision-makers and authorities to develop better mitigation and adaptation strategies to reduce losses caused by these events.

Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated the relationship between vegetation productivity and dry/wet conditions, the capability of different drought indices of assessing the influence of water deficit is not well understood. Moreover, few studies consider the effects of drought on vegetation with a focus on periods of drought. Here, we investigated the spatial-temporal patterns of GPP, the standardized precipitation evapotranspiration index (SPEI), and the vapor pressure deficit (VPD) in China from 2001 to 2020 and examined the relationship between GPP and water deficit/drought for different vegetation types. The results revealed that SPEI and GPP were positively correlated over approximately 70.7% of the total area, and VPD was negatively correlated with GPP over about 66.2% of the domain. Furthermore, vegetation productivity was more negatively affected by water deficit in summer and autumn. During periods of drought, the greatest negative impact was on deciduous forests and croplands, and woody savannas were the least impacted. This research provides a scientific reference for developing mitigation and adaptation measures to lessen the impact of drought disasters under a changing climate.

The European Organization for the Exploitation of Meteorological Satellites’ (EUMETSAT) Meteosat satellites provide the unique opportunity to compile a 30+ year land surface temperature (LST) climate data record. Since the Meteosat instrument on-board Meteosat 2–7 is equipped with a single thermal channel, single-channel LST retrieval algorithms are used to ensure consistency across Meteosat satellites. The present study compares the performance of two single-channel LST retrieval algorithms: (1) A physical radiative transfer-based mono-window (PMW); and (2) a statistical mono-window model (SMW). The performance of the single-channel algorithms is assessed using a database of synthetic radiances for a wide range of atmospheric profiles and surface variables. The two single-channel algorithms are evaluated against the commonly-used generalized split-window (GSW) model. The three algorithms are verified against more than 60,000 LST ground observations with dry to very moist atmospheres (total column water vapor (TCWV) 1–56 mm). Except for very moist atmospheres (TCWV > 45 mm), results show that Meteosat single-channel retrievals match those of the GSW algorithm by 0.1–0.5 K. This study also outlines that it is possible to put realistic uncertainties on Meteosat single-channel LSTs, except for very moist atmospheres: simulated theoretical uncertainties are within 0.3–1.0 K of the in situ root mean square differences for TCWV < 45 mm.

The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts of the spectrum (in the form of Normalized Difference Vegetation Index, NDVI); and (ii) the Thermal Condition Index (TCI), based on top of atmosphere thermal infrared (TIR) brightness temperature or on TIR-derived Land Surface Temperature (LST). VHI is then estimated as a weighted average of VCI and TCI. However, the optimum weights of the two components are usually not known and VHI is usually estimated attributing a weight of 0.5 to both. Using a previously developed methodology for the Euro-Mediterranean region, we show that the multi-scalar drought index (SPEI) may be used to obtain optimal weights for VCI and TCI over the area covered by Meteosat satellites that includes Africa, Europe, and part of South America. The procedure is applied using clear-sky Meteosat Climate Data Records (CDRs) and all-sky LST derived by combining satellite and reanalysis data. Results obtained present a coherent spatial distribution of VCI and TCI weights when estimated using clear- and all-sky LST. This study paves the way for the development of a future VHI near-real time operational product for drought monitoring based on information from Meteosat satellites.

Algorithms for Land Surface Temperature (LST) retrieval from infrared measurements are usually sensitive to the amount of water vapor present in the atmosphere. The Satellite Application Facilities on Climate Monitoring and Land Surface Analysis (CM SAF and LSA SAF) are currently compiling a 25 year LST Climate data record (CDR), which uses water vapor information from ERA-Int reanalysis. However, its relatively coarse spatial resolution may lead to systematic errors in the humidity profiles with implications in LST, particularly over mountainous areas. The present study compares LST estimated with three different retrieval algorithms: a radiative transfer-based physical mono-window (PMW), a statistical mono-window (SMW), and a generalized split-windows (GSW). The algorithms were tested over the Alpine region using ERA-Int reanalysis data and relied on the finer spatial scale Consortium for Small-Scale Modelling (COSMO) model data as a reference. Two methods were developed to correct ERA-Int water vapor misestimation: (1) an exponential parametrization of total precipitable water (TPW) appropriate for SMW/GSW; and (2) a level reduction method to be used in PMW. When ERA-Int TPW was used, the algorithm missed the right TPW class in 87% of the cases. When the exponential parametrization was used, the missing class rate decreased to 9%, and when the level reduction method was applied, the LST corrections went up to 1.7 K over the study region. Overall, the correction for pixel orography in TPW leads to corrections in LST estimations, which are relevant to ensure that long-term LST records meet climate requirements, particularly over mountainous regions.

Land surface temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This study analyzes calibration strategies while considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere, indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis—Satellite Application Facility to calibrate its LST algorithms, applied both for current and forthcoming sensors.