Gridded Climate Research Articles

Plant origin and trade-off between generative and vegetative reproduction determine germination behaviour of a dominant grass species along climatic gradients

An increasing number of studies explore the effects of climate change on plants; less studies have paid attention to germination. Even less is known on a trade-off between investment to generative and vegetative reproduction. To assess how conditions of plant origin, germination climate and seed mass affect species’ germination, we studied Festuca rubra originating from localities situated along a natural climatic grid with a factorial combination of temperatures and precipitations. Germination of seeds of different origin was tested in two temperature and two soil moisture regimes, simulating extremes of the climatic grid. To study the relationship between generative and vegetative reproduction, we used previous data on vegetative traits of the species originating from the same study system. Higher seed mass and warm germination temperature contributed to higher and faster germination. However, the effects of temperature and moisture interacted leading to low germination and high proportion of dormant seeds in warm, dry conditions. Seeds of plants originating from warm climate had higher germination than those from cold climate; the effects of original climate were partly due to differences in seed mass. Total germination negatively correlated with plant performance variables contributing to vegetative reproduction. Both conditions of plant origin and of germination determine species’ germination and the effect of plant origin can partly be explained by seed mass. We confirmed a trade-off between generative and vegetative reproduction indicating that, depending on the actual conditions, the species is able to modify investments to generative or vegetative reproduction.

Climatic conditions and household food security: Evidence from Tanzania

Food security and adequate nutrition are critical for achieving progress toward sustainable development. Two billion people worldwide experience moderate to severe food insecurity, and rates of hunger have increased over the past several years after declining steadily for decades. The FAO attributes this increase in large part to climate change, though empirical evidence on the relationship between climatic conditions and food security remains limited. We examine this question by linking nationally representative longitudinal data from four rounds of the Tanzania National Panel Survey to high-resolution gridded climate data. We then estimate a set of fixed effects regression models to understand the linkages between recent rainy season precipitation and temperature and two indicators of household food security: Food Consumption Score (FCS) and reduced Coping Strategies Index (rCSI). We find that low rainfall—particularly dry and cool conditions—is negatively associated with food security. Moving from a typical rainfall year to a particularly dry one increases the risk of being food insecure on both measures simultaneously by 13 percentage points. This suggests that a lack of rainfall impedes households’ ability to access food, likely through reduced agricultural production and increased food prices, leading to lower dietary diversity and food shortages. Vulnerability is higher among households with fewer working age members, suggesting that households with a greater supply of labor can better withstand droughts. As climate change alters precipitation and temperature patterns over the coming decades, policies to increase resilience will be critical for improving food security, particularly among populations heavily reliant on agriculture.

Observed and projected global warming pressure on coastal hypoxia

Abstract. Coastal hypoxia is a major environmental problem of increasing severity. A global 40-year observational gridded climate data record and 21st-century projections from the Community Earth System Model (CESM) under RCP8.5 (Representative Concentration Pathway) forcing are analyzed for long-term linear trends in summer-month conditions, with a focus on warming-related pressures on coastal oxygen levels. Projected surface temperature and oxygen conditions are compared to global observations over the 16-year overlapping period (2006–2021). Median linear trends for 2006–2100 along the global coast are 0.32 ∘C, −1.6, and −1.2 mmol m−3 per decade for sea-surface temperature (SST), oxygen saturation concentration at the surface (surface oxygen capacity), and vertical-minimum oxygen concentration, respectively. These trends point to more rapid deterioration in coastal conditions than experienced over recent decades; the projected median coastal trends for SST and oxygen capacity are 148 % and 118 % of the corresponding observed rates. Companion analysis of other models and climate scenarios indicates projected coastal oxygen trends for the more moderate RCP4.5 and updated SSP5–8.5 (Shared Socioeconomic Pathway) scenarios, respectively, are 37 %–77 % and 103 %–196 % of the CESM RCP8.5 projections. Median rates for the coast and documented hypoxic areas are higher than in the global ocean. Warming and oxygen declines tend to be fastest at high latitudes, one region where new hypoxic areas may emerge as oxygen conditions deteriorate. There is considerable pressure on current hypoxic areas, since future oxygen declines of any magnitude will make hypoxia more severe. The projections can inform coastal environmental management strategies to protect future water quality and ecosystem services.

Evaluation of Present-Day CMIP6 Model Simulations of Extreme Precipitation and Temperature over the Australian Continent

Australia experiences a variety of climate extremes that result in loss of life and economic and environmental damage. This paper provides a first evaluation of the performance of state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) in simulating climate extremes over Australia. Here, we evaluate how well 37 individual CMIP6 GCMs simulate the spatiotemporal patterns of 12 climate extremes over Australia by comparing the GCMs against gridded observations (Australian Gridded Climate Dataset). This evaluation is crucial for informing, interpreting, and constructing multimodel ensemble future projections of climate extremes over Australia, climate-resilience planning, and GCM selection while conducting exercises like dynamical downscaling via GCMs. We find that temperature extremes (maximum-maximum temperature -TXx, number of summer days -SU, and number of days when maximum temperature is greater than 35 °C -Txge35) are reasonably well-simulated in comparison to precipitation extremes. However, GCMs tend to overestimate (underestimate) minimum (maximum) temperature extremes. GCMs also typically struggle to capture both extremely dry (consecutive dry days -CDD) and wet (99th percentile of precipitation -R99p) precipitation extremes, thus highlighting the underlying uncertainty of GCMs in capturing regional drought and flood conditions. Typically for both precipitation and temperature extremes, UKESM1-0-LL, FGOALS-g3, and GCMs from Met office Hadley Centre (HadGEM3-GC31-MM and HadGEM3-GC31-LL) and NOAA (GFDL-ESM4 and GFDL-CM4) consistently tend to show good performance. Our results also show that GCMs from the same modelling group and GCMs sharing key modelling components tend to have similar biases and thus are not highly independent.

“We could have been leaders”: The rise and fall of offshore wind energy on the political agenda in Ireland

Many countries have ambitious plans for increasing the generation of electricity from offshore wind energy in the coming decades. Realising these ambitions requires concerted, often innovative, political action across several policy domains. To understand the unique political challenges facing offshore wind deployment, this article presents a case study of the Republic of Ireland, where decades of sporadic political ambition to support offshore wind energy has not yet translated into commercial deployment. We use the Multiple Streams Framework to guide data collection, 29 elite interviews and extensive documentary analysis, and offer an historical explanation for why political actors did not meet long-standing market ambition. We analyse the battle of ideas between alternative policy approaches that emerge through distinct policy networks and institutions spanning marine planning, grid development, and energy price support. In Ireland, two key drivers restrained political support for offshore wind for over a decade: competition with onshore wind for limited grid connection capacity and resilience, and calibration of policy instruments to attain decadal renewable energy target at least cost. The study shows the value of a deviant case to demonstrate the political complexities of developing and adopting technology-specific policy instruments to support offshore wind energy in the context of long-term climate change targets, grid development, and grid system services plans.

Hydrological evaluation of gridded climate datasets in a texas urban watershed using soil and water assessment tool and artificial neural network

Precipitation is a vital component of the hydrologic cycle, and successful hydrological modeling largely depends on the quality of precipitation input. Gridded precipitation datasets are gaining popularity as a convenient alternative for hydrological modeling. However, many of the gridded precipitation data have not been adequately assessed across a range of conditions. This study compared three gridded precipitation datasets, Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), and Parameter-elevation Relationships on Independent Slopes Model (PRISM). This study used the conventional gauge observation as reference data and evaluated the suitability of the three sources of gridded rainfall data to drive rainfall-runoff simulations. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) were used to create daily streamflow simulations in the Leon Creek Watershed (LCW) in San Antonio, Texas, with the TRMM, CFSR, PRISM, and gauge rainfall data used as inputs. A direct comparison of the gridded data sources showed that the TRMM data underestimates the volume of rainfall, while PRISM data most closely matches the volume of rainfall when compared to the gauge rainfall observations. The hydrological simulation results showed that the PRISM and TRMM rainfall data driven models had preferable results to the CFSR and gauge driven models, in terms of both graphical comparison and goodness-of-fit indicator values. Additionally, no significant discrepancy was found between SWAT and ANN simulation results when the same precipitation data source was used, while SWAT and ANN simulation results varied in an identical pattern when different precipitation data sources were applied.

Forecasting Live Fuel Moisture of Adenostema fasciculatum and Its Relationship to Regional Wildfire Dynamics across Southern California Shrublands

In seasonally dry environments, the amount of water held in living plant tissue—live fuel moisture (LFM)—is central to vegetation flammability. LFM-driven changes in wildfire size and frequency are particularly important throughout southern California shrublands, which typically produce intense, rapidly spreading wildfires. However, the relationship between spatiotemporal variation in LFM and resulting long-term regional patterns in wildfire size and frequency within these shrublands is less understood. In this study, we demonstrated a novel method for forecasting the LFM of a critical fuel component throughout southern California chaparral, Adenostema fasciculatum (chamise) using gridded climate data. We then leveraged these forecasts to evaluate the historical relationships of LFM to wildfire size and frequency across chamise-dominant California shrublands. We determined that chamise LFM is strongly associated with fire extent, size, and frequency throughout southern California shrublands, and that LFM–wildfire relationships exhibit different thresholds across three distinct LFM domains. Additionally, the cumulative burned area and number of fires increased dramatically when LFM fell below 62%. These results demonstrate that LFM mediates multiple aspects of regional wildfire dynamics, and can be predicted with sufficient accuracy to capture these dynamics. Furthermore, we identified three distinct LFM ‘domains’ that were characterized by different frequencies of ignition and spread. These domains are broadly consistent with the management thresholds currently used in identifying periods of fire danger.

Evaluation of Future Maize Yield Changes and Adaptation Strategies in China

In the past century, climate change has become more significant, which has a great impact on crop growth, especially food security. Based on the regional climate model PRECIS, high-precision grid climate data in China under RCP4.5 and RCP8.5 scenarios were output, and the high-precision amplification and calibration of crop model DSSAT were calibrated and verified in combination with data of maize planting from 2005 to 2015, including observation data of agrometeorological stations, ecological networking experiment data and maize survey data of agricultural demonstration counties. The impact of climate change on maize production in 2030s and 2050s was evaluated; and the effect of main adaptation strategies to climate change is put forward which could support macro strategies of layout adjustment for the maize production system. The results show that if no countermeasures are taken in the future, the risk of maize yield reduction in China will gradually increase, especially under the RCP8.5 scenario. The risk of maize yield reduction in each main production area will be very prominent in the 2050s under the RCP8.5 scenario, which would be between 10–30%. Compared with a delayed sowing date, an early sowing date would be more conducive to maize production, but there would be some differences in different regions. The heat in the growing season of maize would increase significantly. If the growth time of maize from silking to maturity could be prolonged and the accumulated temperature could be raised, the dry matter accumulation of maize would effectively increase, which would have an obvious effect on yield. Improving grain filling rate is also significant, although the effect of yield increase would be smaller. Therefore, sowing in advance, full irrigation and cultivating varieties with a long reproductive growth period could effectively alleviate the yield reduction caused by climate change. Adjusting maturity type and grain harvest strategy would have a more obvious mitigation effect on yield reduction in northeast China and northern China, and plays a positive role in ensuring future maize yield.

The role of plant functional groups mediating climate impacts on carbon and biodiversity of alpine grasslands

Plant removal experiments allow assessment of the role of biotic interactions among species or functional groups in community assembly and ecosystem functioning. When replicated along climate gradients, they can assess changes in interactions among species or functional groups with climate. Across twelve sites in the Vestland Climate Grid (VCG) spanning 4 °C in growing season temperature and 2000 mm in mean annual precipitation across boreal and alpine regions of Western Norway, we conducted a fully factorial plant functional group removal experiment (graminoids, forbs, bryophytes). Over six years, we recorded biomass removed, soil microclimate, plant community composition and structure, seedling recruitment, ecosystem carbon fluxes, and reflectance in 384 experimental and control plots. The dataset consists of 5,412 biomass records, 360 species-level biomass records, 1,084,970 soil temperature records, 4,771 soil moisture records, 17,181 plant records covering 206 taxa, 16,656 seedling records, 3,696 ecosystem carbon flux measurements, and 1,244 reflectance measurements. The data can be combined with longer-term climate data and plant population, community, ecosystem, and functional trait data collected within the VCG.

Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea

There is a large number of grid-based climate datasets available which differ in terms of their data source, estimation procedures, and spatial and temporal resolutions. This study evaluates the performance of diverse meteorological datasets in terms of representing spatio-temporal climate variabilities based on a national-scale domain over South Korea. Eleven precipitation products, including six satellite-based data (CMORPH, MSWEP, MERRA, PERSIANN, TRMM, and TRMM-RT) and five reanalysis-based data (ERA5, JRA-55, CPC-U, NCEP-DOE, and K-Hidra) and four temperature products (MERRA, ERA5, CPC-U, and NCEP-DOE) are investigated. In addition, the hydrological performance of forty-four input combinations of climate datasets are explored by using the Variable Infiltration Capacity (VIC) macroscale model. For this analysis, the VIC model is independently calibrated for each combination of input and the response to each combination is then evaluated with in situ streamflow data. Our results show that the gridded datasets perform differently particularly in representing precipitation variability. When a diverse combination of the datasets are used to represent spatio-temporal variability of streamflow through the hydrological model, K-Hidra and CPC-U performed best for precipitation and temperature, followed by the MERRA and ERA5 datasets, respectively. Lastly, we obtain only marginal improvement in the hydrological performance when utilizing multiple climate datasets after comparing it to a single hydrological simulation with the best performing climate dataset. Overall, our results indicate that the hydrological performance may vary considerably based on the selection of climate datasets, emphasizing the importance of regional evaluation studies for meteorological datasets.

Changing compound rainfall events in Tasmania

AbstractWhile extreme weather and climate events have been studied for several decades, analysis of compound events has only begun in recent years. In this burgeoning field there are still many open questions around the optimal methodology and analysis tools for analysis. After consultation with state emergency services in Tasmania, Australia, we examined which compound events have the largest impacts on their organizations. Through this consultation process we found that many of the severe flooding events in the state do not coincide with the highest rainfall days. Flooding on intense rainfall days is well understood, but flooding can also occur on days where the rainfall is not particularly extreme, especially if catchments are already saturated. Using the Australian Gridded Climate Data and six dynamically downscaled, Representative Concentration Pathway 8.5, bias adjusted Coupled Model Intercomparison Project 5 models we developed a method to quantify such compounding events to examine how they are changing from 1961 to 2100. We optimized a pre‐existing technique to estimate the antecedent conditions in catchments, combined with daily rainfall. We found that during 1961–2017, the number of compound rainfall events has been decreasing in the four Tasmanian catchments we studied, although the trend was statistically significant in only one case. The intensity of compound rainfall events was found to have increased significantly in some areas. Many future projections place Tasmania at the boundary of a drying trend to the west and wetting trend to the east and the position of this boundary varies between models leading to contrasting projected changes for parts of Tasmania. However, there is projected to be a decline in rainfall to 2100 associated with the southward shift in the storm‐track. Compound rainfall events are projected to decline throughout Tasmania, except in the south which will remain stable to 2100. The intensities are projected to increase in the south and decrease in the west, related to the changing thermodynamics and dynamics of rainfall drivers in the region in a warmer climate.

Applying Machine Learning for Threshold Selection in Drought Early Warning System

This study investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and meteorological drought category to identify NDVI thresholds that correspond to varying drought categories. The gridded evaluation was performed across a 34-year period from 1982 to 2016 on a monthly time scale for Grassland and Temperate regions in Australia. To label the drought category for each grid inside the climate zone, we use the Australian Gridded Climate Dataset (AGCD) across a 120-year period from 1900 to 2020 on a monthly scale and calculate percentiles corresponding to drought categories. The drought category classification model takes NDVI data as the input and outputs of drought categories. Then, we propose a threshold selection algorithm to distinguish the NDVI threshold to indicate the boundary between two adjacent drought categories. The performance of the drought category classification model is evaluated using the accuracy metric, and visual interpretation is performed using the heat map. The drought classification model provides a concept to evaluate drought severity, as well as the relationship between NDVI data and drought severity. The results of this study demonstrate the potential application of this concept toward early drought warning systems.

Mid-Holocene high-resolution temperature and precipitation gridded reconstructions over China: Implications for elevation-dependent temperature changes

Driven by enhanced summer insolation in the Northern Hemisphere and land-atmosphere feedbacks during the mid-Holocene (MH), most regions of China were characterized by warmer and wetter summer than present. The MH has been recognized as a benchmark period for climate simulations, but proxy-based reconstructions and model results show some key discrepancies in climate reconstructions. A lack of accurate gridded climate data is a major obstacle to land surface modelling, which is contributing to uncertainties in climate simulations. Here we developed a monthly gridded dataset of temperature and precipitation for MH China through applying eight interpolation methods to 159 pollen-based climate reconstructions for January and July from previous studies. The gridded dataset is consistent with these input records and other independent climate reconstructions at 62 sites. Then, the temperature differences between the MH and pre-industrial period are correlated with elevations in China. Results show a significant elevation-dependent temperature amplification of 0.59°C/km for July during the MH relative to pre-industrial period. In addition, our gridded reconstructions of the MH climate were compared with climate simulations for the MH and for the future medium-emission scenario. Most simulations significantly underestimated the magnitude of climate anomalies during the MH in China. Similar to the MH, a pronounced warming in winter and a strong wetting in summer are projected in China at the end of this century (i.e., 2091–2100), suggesting analogous seasonal changes during the MH and future. This first high-resolution proxy-based gridded climate dataset for MH China can be used in land surface modelling to improve our understanding of terrestrial changes during the MH, which in turn is expected to facilitate the climate simulations.

Evaluation of Monthly Precipitation Data from Three Gridded Climate Data Products over Nigeria

Evaluation of Monthly Precipitation Data from Three Gridded Climate Data Products over Nigeria

Climate change and child malnutrition: A Nigerian perspective

Erratic temperatures and precipitation influence nutrition, human capital investment, and living standards, particularly for children. This study investigates the effect of climate change (changes in the monthly maximum average near-surface temperature and total monthly precipitation) on children’s health outcomes, particularly stunting and underweight, in Nigeria. We combine Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) data with high resolution gridded climate data. We find that the rise in temperature is associated with higher levels of stunting – even more so in rural areas. The paper’s findings highlight the need for climate-friendly policies to mitigate the long-term effect of climate change on malnourishment. Without such policies, climate change could reverse years of progress in lowering children’s malnutrition.

Topographic and vegetation drivers of thermal heterogeneity along the boreal-grassland transition zone in western Canada: Implications for climate change refugia.

Climate change refugia are areas that are relatively buffered from contemporary climate change and may be important safe havens for wildlife and plants under anthropogenic climate change. Topographic variation is an important driver of thermal heterogeneity, but it is limited in relatively flat landscapes, such as the boreal plain and prairie regions of western Canada. Topographic variation within this region is mostly restricted to river valleys and hill systems, and their effects on local climates are not well documented. We sought to quantify thermal heterogeneity as a function of topography and vegetation cover within major valleys and hill systems across the boreal–grassland transition zone.Using iButton data loggers, we monitored local temperature at four hills and 12 river valley systems that comprised a wide range of habitats and ecosystems in Alberta, Canada (N = 240), between 2014 and 2020. We then modeled monthly temperature by season as a function of topography and different vegetation cover types using general linear mixed effect models.Summer maximum temperatures (T max) varied nearly 6°C across the elevation gradient sampled. Local summer mean (T mean) and maximum (T max) temperatures on steep, north‐facing slopes (i.e., low levels of potential solar radiation) were up to 0.70°C and 2.90°C cooler than highly exposed areas, respectively. T max in incised valleys was between 0.26 and 0.28°C cooler than other landforms, whereas areas with greater terrain roughness experienced maximum temperatures that were up to 1.62°C cooler. We also found that forest cover buffered temperatures locally, with coniferous and mixedwood forests decreasing summer T mean from 0.23 to 0.72°C and increasing winter T min by up to 2°C, relative to non‐forested areas.Spatial predictions of temperatures from iButton data loggers were similar to a gridded climate product (ClimateNA), but the difference between them increased with potential solar radiation, vegetation cover, and terrain roughness.Species that can track their climate niche may be able to compensate for regional climate warming through local migrations to cooler microsites. Topographic and vegetation characteristics that are related to cooler local climates should be considered in the evaluation of future climate change impacts and to identify potential refugia from climate change.

Long‐term meteorological drought characterization in the São Francisco watershed, Brazil: A climatic water balance approach

AbstractPartially located in the Brazilian Semiarid region, the São Francisco watershed (SFW) is a strategic hydrological system for power generation, agricultural development and the integration of the national territory. Droughts are recurrent in the Brazilian Semiarid, and climate change projected scenarios indicate their intensification in the SFW, which may compromise the access to its resources. In this context, the objective of this study was to characterize long‐term spatial and temporal meteorological drought patterns over the SFW while accounting for its different climates. We used gridded Climate Research Unit Time Series rainfall and potential evapotranspiration (PET) data for the 1942–2019 (78 years) period to retrieve a monthly climatic water balance‐based drought index. The climatological aspects of drought incidence in the SFW were assessed through linear trend analysis, the estimation of the relative effects of precipitation and PET trends in drought occurrence, and the identification of the main interannual variability modes controlling the triggering of meteorological drought through wavelet functions. Results showed that water deficit periods are becoming more frequent and intense, affecting larger areas in the middle (tropical climate) and lower (semiarid climate) portions of the SFW. Months with water surplus in these regions are also becoming less frequent. In the upper (humid subtropical climate) SFW, evidences show that water deficit periods are becoming more frequent, with an expansion of areas affected by droughts. In this subregion, increasing PET trends played a more important role than precipitation trends in the propagation of drought. Finally, low‐frequency teleconnection mechanisms such as the El Niño–Southern Oscillation were found to drive the occurrence of drought in most of the SFW, while higher frequency oscillations (such as the Atlantic Meridional Mode) play a role in the lower latitudes of the basin. In the upper SFW, results indicated that interannual variability might be driven by oscillations with stronger intraseasonal components.

Incorporating Empirical Orthogonal Function Analysis into Machine Learning Models for Streamflow Prediction

Machine learning (ML) models have been widely used to predict streamflow. However, limited by the high dimensionality and training difficulty, high-resolution gridded climate datasets have rarely been used to build ML-based streamflow models. In this study, we developed a general modeling framework that applied empirical orthogonal function (EOF) analysis to extract information from gridded climate datasets for building ML-based streamflow prediction models. Four classic ML methods, namely, support vector regression (SVR), multilayer perceptron (MLP), long short-term memory (LSTM) and gradient boosting regression tree (GBRT), were incorporated into the modeling framework for performance evaluation and comparison. We applied the modeling framework to the upper Heihe River Basin (UHRB) to simulate a historical 22-year period of daily streamflow. The modeling results demonstrated that EOF analysis could extract the spatial information from the gridded climate datasets for streamflow prediction. All four selected ML models captured the temporal variations in the streamflow and reproduced the daily hydrographs. In particular, the GBRT model outperformed the other three models in terms of streamflow prediction accuracy in the testing period. The R2, RMSE, MAE, NSE and PBIAS were equal to 0.68, 9.40 m3/s, 5.18 m3/s, 0.68 and −0.03 for the daily streamflow in the Taolai River Watershed of the UHRB, respectively. Additionally, the LSTM method could provide physically based hydrological explanations of climate predicators in streamflow generation. Therefore, this study demonstrated the unique capability and functionality of incorporating EOF analysis into ML models for streamflow prediction, which could make better use of the readily available gridded climate data in hydrological simulations.

Mcera5: Driving microclimate models with ERA5 global gridded climate data

Abstract Microclimate models predict temperature and other meteorological variables at scales relevant to individual organisms. The broad application of microclimate models requires gridded macroclimatic variables as input. However, the spatial and temporal resolution of such inputs can be a limiting factor on the accuracy of microclimate predictions. Due to its fine resolution and accuracy, the ERA5 reanalysis dataset is emerging as the favoured resource for global historical weather and climate data and has great potential for aiding microclimate modelling. Here we describe mcera5, an R language package that provides convenient access to, and wrangling of, the ERA5 climate datasets for use in microclimate models. Through this package, we provide functions to query ERA5 data for desired spatial and temporal extents, to correct for spatial biases and process outputs for easy interpretation by ecologists, thereby allowing faster and more accurate microclimate predictions. By validating with empirical observations from multiple biomes globally, we demonstrate that the use of ERA5 climate forcing via mcera5 improves the prediction accuracy of soil moisture, air temperature and relative humidity as compared to forcing with other globally available data and offers comparable performance when predicting soil temperatures. Through the provision of fine‐resolution ERA5 data, the mcera5 package fits into an ecosystem of tools for modelling microclimate in a spatio‐temporally explicit fashion, advancing our ability to efficiently predict microclimate for any place on Earth for the past, present or future. The package also provides convenient access to ERA5 datasets for a range of other applications.

A Probabilistic Multi-Source Remote Sensing Approach to Evaluate Extreme Precursory Drought Conditions of a Wildfire Event in Central Chile

Forest fires are a major issue worldwide, and especially in Mediterranean ecosystems where the frequency, extension and severity of wildfire events have increased related to longer and more intense droughts. Open access remote sensing and climate datasets make it possible to describe in detail the precursory environmental conditions triggering major fire events under drought conditions. In this study, a probabilistic methodological approach is proposed and tested to evaluate extreme drought conditions prior to the occurrence of a wildfire in Central Chile, an area suffering an unprecedented prolonged drought. Using 21 years of monthly records of gridded climate and remotely sensed vegetation water status data, we detected that vegetation at the ground level, by means of fine and dead fuel moisture (FDFM), and canopy level, by means of the enhanced vegetation index (EVI) were extremely dry for a period of about 8 months prior to the fire event, showing records that fall into the 2.5% of the lowest values recorded in 21 years. These extremely dry conditions of the vegetation, consequence of low air humidity and precipitation, favored the ignition and horizontal and vertical propagation of this major wildfire. Post fire, we found high severity values for the native vegetation affected by the fire, with dNBR values >0.44 3 days after the fire and significant damage to the Mediterranean sclerophyllous and deciduous forest present in the burned area. The proposed probabilistic model is presented as an innovation and an alternative to evaluate not only anomalies of the meteorological and vegetation indices that promote the generation of extreme events, but also how unusual or extreme these conditions are. This is achieved by placing the abnormal values in the context of the reference historical frequency distribution of all available records, in this case, more than 20 years of remote sensing and climate data. This methodology can be widely applied by fire researchers to identify critical precursory fire conditions in different ecosystems and define environmental indicators of fire risk.