Gridded Climate Research Articles

Lidar-derived estimates of boreal shrub biomass in Southcentral Alaska

Abstract Despite widespread observations of shrub proliferation and expansion (shrubification), few studies quantify shrub biomass at the regional scale. Here we describe and implement a two-part modeling approach to estimate and map tall shrub (diameter at root collar > 2.5 cm) expected aboveground biomass, or E[SHB], across a 16.6 million ha boreal region where shrubification occurs in wetlands and subalpine ecosystems. Using n = 384 field plots nested within m= 11 study sites across southcentral Alaska, we constructed random forest models of the probability (pSH) that shrub wood volume surpasses tree wood volume, and generalized additive models of aboveground biomass of tall shrubs (SHB) using rasterized aerial lidar variables collected by NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, together with gridded climate data from a Parameter-elevation Regressions on Independent Slopes Model 30-year normal climatology (PRISM). Applying those models to G-LiHT tiles, then averaging across tiles within n = 843 watersheds covering 9.2 million ha, we estimated that below 1000 m asl, the area-weighted mean value of E[SHB] = pSH x SHB = 6.6 Mg ha−1 with sd = 4.5 Mg ha−1. This is the first study to estimate current shrub biomass density at the regional scale in southcentral Alaska and serves as a biomass baseline for measuring and modeling aboveground carbon fluxes where plant communities are undergoing climate-driven change.

A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study

AbstractAgrometeorological data are essential for understanding production using digital agriculture techniques. However, integrating agrometerological observations from multiple sources remains a challenge. Often, digital agriculture scientists download and clean the same datasets many times. We present a prototype system that simplifies the process of collecting, cleaning, integrating, and aggregating data from meteorological data sources by providing a simplified user interface, database, and application programming interface. The prototype provides a standard interface for querying multiple geospatial formats (raster and vector) and integrates observation networks including the National Oceanic and Atmospheric Administration Global Historical Climatology Network (NOAA GHCN), NOAA NClim‐Grid (NOAA's Gridded Climate Normals), and Ameriflux BASE. The system automatically checks and updates data, saving storage space and processing time, and allows users to summarize data spatially and temporally. Provided as open source code and browser‐based user interface, the application and integration system can be run across Windows, Linux, and Mac environments to support broader use of multi‐source agrometeorology data.

Phenology forecasting models for detection and management of invasive annual grasses

AbstractNon‐native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well‐timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range‐wide phenology forecasts for two problematic invasive annual grasses: cheatgrass (Bromus tectorum), and red brome (Bromus rubens). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat‐insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within‐season or within‐year decision support.

Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin

Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.

Assessing climate trends in the Northwestern Himalayas: a comprehensive analysis of high-resolution gridded and observed datasets

Climate change poses significant challenges to the Himalayas, a region characterised by its fragile ecosystems and vulnerable communities dependent on environmental resources. Accurate climate data are crucial for understanding regional climatic variations and assessing climate change impacts, particularly in areas with limited observational networks. This study represents a pioneering effort in evaluating climatic fluctuations in the Jhelum basin, located in the North Western Himalayas, by utilising a diverse range of gridded meteorological datasets (APHRODITE, CHIRPS, CRU, and IMDAA) alongside observed climate data from the Indian Meteorological Department. The primary goal is to identify the most effective gridded climate data product for regions with limited data and to explore the potential of combining gridded data sets with observed data to understand climatic variability. Findings indicate a consistent upward trend in temperature across all datasets, with varying rates of increase. CRU records a rise of 1 °C in Tmax and 1.6 °C in Tmin, while APHRODITE shows a Tmean increase of approximately 1 °C. IMDAA reports increases in Tmax and Tmin. Observed mean annual Tmax and Tmin show net increases of 1 °C and 0.6 °C, respectively. Regarding precipitation, all datasets except IMDAA exhibit an increasing trend, contrary to observed data, which decreases from 1266 mm to 1068 mm over 40 years. CHIRPS, CRU, and APHRODITE display increasing trends, while IMDAA aligns closely with observed data but tends to overestimate precipitation by about 30%. Our research identifies IMDAA as the most suitable gridded climate data for the Jhelum basin in the North-western Himalayas. Despite some discrepancies in precipitation trends, IMDAA closely aligns with observed data, providing valuable insights for scholars and policymakers navigating climate data uncertainties in complex environments. Our findings contribute to informed decision-making and effective climate change mitigation strategies in the region.

A Statistical Analysis of Drought and Fire Weather Indicators in the Context of Climate Change: The Case of the Attica Region, Greece

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change.

A Comprehensive Evaluation of Mean and Extreme Climate for the Conformal Cubic Atmospheric Model (CCAM)

Abstract This study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5) reanalysis data from 1985 to 2014, following a 5-yr spinup period. It focuses on daily maximum and minimum temperatures and daily precipitation, comparing CCAM to ERA5 and the Australian Gridded Climate Data (AGCD). The CCAM effectively reduces warm biases in daily minimum temperatures but struggles with cold biases in daily maximum temperatures, particularly in northern Australia during the wet season, possibly due to high-level cloud overestimation. Precipitation tends to be overestimated, especially in extreme rainfall events, though offset by an underestimation of low rainfall. The study showcases improvements in the annual minimum of daily minimum temperatures across most of Australia, while identifying challenges in forecasting cooler extreme temperatures. It adds value to annual maximum daily maximum temperatures in southern Australia but less so in the north. The analysis of the 5% annual exceedance probability (AEP5%) yields mixed results influenced by location and potential ocean temperature changes. Some coastal areas exhibit lost value, possibly linked to ocean temperature shifts. Furthermore, CCAM’s representation of maximum annual daily and 5-day rainfall reveals lost value, particularly in eastern Australia due to an overestimate of extreme rainfall. Despite the challenges of comparing a dynamical downscaling model like CCAM to ERA5, this study highlights its benefits in reducing biases, especially in temperature representation. Given the larger biases in phase 6 of Coupled Model Intercomparison Project (CMIP6) global climate models, CCAM appears suitable for dynamic downscaling in climate projections, emphasizing the need for ongoing model enhancements, including addressing biases related to ephemeral water bodies and extreme rainfall. Significance Statement This study critically assesses the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling ERA5 reanalysis data from 1985 to 2014, offering valuable insights into climate modeling. Focusing on temperature and precipitation, CCAM proves effective in mitigating warm biases in daily minimum temperatures but encounters challenges with cold biases in daily maximum temperatures, particularly in northern Australia. The analysis reveals the overestimation of precipitation, especially in extreme events, yet identifies improvements in annual minimum daily minimum temperatures across Australia. The study underscores CCAM’s potential in reducing biases compared to CMIP6 global climate models, making it a promising tool for dynamic downscaling in climate projections. It emphasizes the necessity for ongoing model enhancements, particularly addressing biases related to ephemeral water bodies and extreme rainfall.

Implications of climate and litter quality for simulations of litterbag decomposition at high latitudes

Abstract. Litter decomposition is a vital part of the carbon cycle and is thoroughly studied both in the field and with models. Although temporally and spatially limited, litterbag decomposition experiments are often used to calibrate and evaluate soil models, coupled to land models, that are intended for use on large scales. We used the microbially explicit soil decomposition model MIMICS+ to replicate two high-latitude litterbag decomposition experiments of different spatial and temporal scales. We investigated how well the model represented observed mass loss in terms of the controlling factors of climate and litter quality and their relative importance with time. In addition to default model forcing, we used measured and site-specific model-derived microclimatic variables (soil moisture and temperature), hypothesizing that this would improve model results. We found that MIMICS+ represented mass loss after 1, 3, and 6 years well across a climatic gradient of Canadian sites but had more variable results for 1-year mass loss across a climate grid in southern Norway. In terms of litter quality, the litter metabolic fraction had more influence on modeled mass loss than the carbon-to-nitrogen ratio of the litter. Using alternative microclimate sources led to up to 23 % more mass remaining and down to 22 % less mass remaining compared to the simulations using default model inputs. None of the input alternatives significantly improved results compared to using the default model setup. We discuss possible causes for our findings and suggest measures to better utilize short-term field experiments to inform microbially explicit decomposition models.

Trend and inter-annual variability in regional climate models – Validation and hydrological implications in southeast Australia

We assessed the ability of Regional Climate Models (RCMs) to reproduce observed means, inter-annual variance and trends for rainfall indices that were important for runoff generation over southeast Australia. To establish the benefit of the RCM without being penalized or rewarded by the performance of the forcing GCM, we used ECMWF Re-Analysis-interim (ERA-Interim) to force the Weather Research and Forecasting model (WRF) and the Conformal Cubic Atmospheric Model (CCAM). The performance of two different configurations of each RCM was evaluated against an observational dataset (Australian Gridded Climate Data, AGCD) and compared with that of ERA-Interim. The assessments were carried out at both observational and ERA-Interim grid resolutions: ∼5 km and ∼80 km, respectively. As hypothesised, the RCMs outperformed ERA-Interim in representing spatial patterns and magnitude of mean rainfall, because of higher spatial resolution. RCMs also accurately represented the general spatial patterns of variance, but systematically underestimated the inter-annual variability over much of the domain. RCMs performed better than ERA-Interim in reproducing the magnitude of trends in some cases, especially in the decline of cool season rainfall in southeast Australia, which has had significant impacts on water resources. One of the two CCAM runs generally performed best across all rainfall indices, in part because of atmospheric spectral nudging and an improved land-surface model. Based on the findings, we have provided suggestions on where new research on the development of RCMs could be focused and recommendations relevant to the next generation of Australian hydro-climate projections generated from the sixth phase of the Coupled Model Intercomparison Project (CMIP6).

Sensitivity of Australian Rainfall to Driving SST Data Sets in a Variable‐Resolution Global Atmospheric Model

AbstractIn this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a variable‐resolution global atmospheric model, driven by two distinct sea surface temperature (SST) data sets: the 0.25° Optimum Interpolation Sea Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is assessed using a benchmarking framework, revealing good agreement between both simulations and the climatological rainfall spatial pattern, seasonality, and annual trends obtained from the Australian Gridded Climate Data (AGCD). Notably, wet biases are identified in both simulations, with CCAM_OISST displaying a more pronounced bias. Furthermore, we have examined CCAM’s ability to capture El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with rainfall during Austral spring (SON) utilizing a novel hit rate metric. Results indicate that only CCAM_OISST successfully replicates observed SON ENSO‐ and IOD‐rainfall correlations, achieving hit rates of 86.6% and 87.5%, respectively, compared to 52.7% and 41.8% for CCAM_ERSST5. Large SST differences are found surrounding the Australian coastline between OISST and ERSST5 (termed the “Coastal Effect”). Differences can be induced by the spatial interpolation error due to the discrepancy between model and driving SST. An additional CCAM experiment, employing OISST with SST masked by ERSST5 in 5° proximity to the Australian continent, underscores the “Coastal Effect” has a significant impact on IOD‐Australian rainfall simulations. In contrast, its influence on ENSO‐Australian rainfall is limited. Therefore, simulations of IOD‐Australian rainfall teleconnection are sensitive to local SST representation along coastlines, probably dependent on the spatial resolution of driving SST.

Climate-based prediction of carbon fluxes from deadwood in Australia

Abstract. Deadwood is an important yet understudied carbon pool in tropical ecosystems. Deadwood degradation to CO2 through decomposer (microbial, termite) activities is driven by wood moisture and temperature, which are in turn strongly influenced by local climate. Thus, climate data could be used to predict CO2 fluxes from decaying wood. Given the increasing availability of gridded climate data, this link would allow for the rapid estimation of deadwood-related CO2 fluxes from tropical ecosystems worldwide. In this study, we adapted a mechanistic fuel moisture model that uses weather variables (e.g., air temperature, precipitation, solar radiation) to simulate wood moisture and temperature along a rainfall gradient in Queensland, Australia. We then developed a Bayesian statistical relationship between wood moisture and temperature and CO2 flux from pine (Pinus radiata) blocks and combined this relationship with our simulations to predict CO2 fluxes from deadwood at 1 h temporal resolution. We compared our pine-based simulations to the moisture–CO2 relationships from stems of native tree species deployed at the wettest and driest sites. Finally, we integrated fluxes over time to estimate the amount of carbon entering the atmosphere and compared these estimates to measured mass loss in pines and native stems. Our statistical model showed a positive relationship between CO2 fluxes and wood moisture and temperature. Comparing cumulative CO2 with wood mass loss, we observed that carbon from deadwood decomposition is mainly released as CO2 regardless of the precipitation regime. At the dry savanna, only about 20 % of the wood mass loss was decomposed within 48 months, compared to almost 100 % at the wet rainforest, suggesting longer residence times of deadwood compared to wetter sites. However, the amount of carbon released in situ as CO2 is lower when wood blocks are attacked by termites, especially at drier sites. These results highlight the important but understudied role of termites in the breakdown of deadwood in dry climates. Additionally, mass loss–flux relationships of decaying native stems deviated from those of pine blocks. Our results indicate that wood moisture and temperature are necessary but not sufficient for predicting CO2 fluxes from deadwood degradation. Other factors, such as wood traits (wood quality, chemical composition, and stoichiometry) and biotic processes, should be considered in future modeling efforts.

Occurrence and trends of historical tropical cyclone rainfall on near-coastal regions of Australia

Extreme rainfall driven by tropical cyclones (TCs) has profound effects on Australian coastlines at both local and regional scales. Here, we develop methods for comparing TC-driven widespread and localised rainfall on three broad coastal regions of tropical Australia (west, north and east). Trends, average recurrence intervals (ARIs) and the fractional contribution of TC rainfall are explored in three historical datasets: Australian Gridded Climate Data (AGCD), ECMWF Reanalysis (ver. 5, ERA5) and the Bureau of Meteorology Atmospheric high resolution Regional Reanalysis for Australia (ver. 1, BARRA1). Results for trends and ARIs between the different datasets are generally inconsistent and also differ between regions, partially owing to the short-term temporal records of some of the data as well as inconsistencies in extreme values between datasets. By contrast, there is a general agreement between all datasets on the fractional contribution of TC rainfall, signalling an increase in recent years. This result is considered together with the trend towards fewer TCs occurring in this region over recent decades, indicating a trend towards increased rainfall intensity per TC on average, assuming steady landfall rates. The methods developed here can be applied easily to other data types such as regional climate model experiments, facilitating a multiple lines of evidence approach that incorporates both observational-based and model-based data. This research is intended to help provide new methods and guidance for identifying trends in TC-driven extreme rainfall, relevant for enhanced planning and adaptation to the impacts of these extreme weather systems.

Statistical Performance of Gridded Rainfall Datasets Over Ungauged Jalaur River Basin, Philippines

The study presented aims to find the most appropriate climate dataset for the data-scarce Jalaur River Basin (JRB), Iloilo, Philippines, by evaluating the statistical performance of five rainfall datasets (APHRODITE, CPC NOAA, ERA5, SA-OBS, and PGF-V3) with resolutions of 0.25° and 0.5° having a time domain of 1981 to 2005. Bilinear interpolation implemented through Climate Data Operator (CDO) was used to extract and process grid climate datasets with Linear scaling as bias correction to minimize product simulation uncertainties. The datasets were compared to the lone meteorological station nearest to JRB investigated at monthly and annual timescales using six statistical metrics, namely, Pearson’s correlation coefficient (r), coefficient of determination (R2), modified index of agreement (d1), Kling-Gupta efficiency, Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results indicate a strong positive correlation with the observed data for both rainfall and temperature (r > 0.8; R2, d1 > 0.80). Although graphical observation shows an underestimation of rainfall, goodness-of-fit values indicate very good model performance (NSE, KGE > 0.75; RSR < 0.50). In terms of temperature, variable responses are observed with significant overestimation for maximum temperature and underestimation for minimum temperature. SA-OBS proved to be the best-performing dataset, followed by ERA5 and PGF-V3. These key findings supply useful information in deciding the most appropriate gridded climate dataset for hydrometeorological investigation in the JRB and could enhance the regional representation of global datasets.

Evaluation of Grid-Based Aridity Indices in Classifying Aridity Zones in Iraq

In this study, the aridity index (AI) based on gridded climate data was validated for defining aridity and classifying aridity zones in Iraq through comparison with the results obtained by the station-based aridity index. Gauge-based gridded climate data taken from Climatic Research Unit Timeseries (CRU TS) were used to determine the annual value of four aridity indices (Lang, De Martonne, Ernic and UNEP AI) over the period 1998-2011. The results showed that the aridity distribution maps derived using grid-based aridity indices were reasonably close to those found using station-based ones. The four aridity indices properly identified similar aridity (dryness) classifications in both the station-based and grid-based aridity maps. The area percentage of each aridity class predicted by grid-based AIs was also compared with that obtained by the station-based AIs. The results showed that the variances between the area percentages predicted by grid-based AIs and those estimated using station-based AIs are fairly slight. The Lang AI exhibited the least variance (0.4%) while the De Martonne AI had the biggest variance (-4.8%). Despite these minor variances, it is however possible to conclude that the grid-based aridity index classified the aridity zones of Iraq as properly as the station-based aridity index did.

A unified dataset for pre-processed climate indicators weighted by gridded economic activity

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (total precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density, night-time light intensity, cropland, and concurrent population count – all proxies of economic activity – before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, and we validate our data through a systematic comparison with those employed in leading climate impact studies.

Genetic response of a perennial grass to warm and wet environments interacts and is associated with trait means as well as plasticity.

Potential for rapid evolution is an important mechanism allowing species to adapt to changing climatic conditions. Although such a potential has been largely studied in various short-lived organisms, to what extent we can observe similar patterns in long-lived plant species, which often dominate natural systems, is largely unexplored. We explored potential for rapid evolution in Festuca rubra, long-lived grass with extensive clonal growth dominating in alpine grasslands. We used field sowing experiment simulating expected climate change in our model region. Specifically, we exposed seeds from 5 independent seed sources to novel climatic conditions by shifting them along a natural climatic grid and explored genetic profiles of established seedling after 3 years. Data on genetic profiles of plants selected under different novel conditions indicate that different climate shifts select significantly different pools of genotypes from common seed pools. Increasing soil moisture was more important selective pressure than increasing temperature or interaction of the two climatic factors. This can indicate negative genetic interaction in response to the combined effects, or that the effects of different climates are interactive rather than additive. The selected alleles were found in genomic regions likely affecting function of specific genes or their expression. Many of these were also linked to morphological traits (mainly to trait plasticity) suggesting these changes may have a consequence for plant performance. Overall, these data indicate that even long-lived plant species may experience strong selection by climate, and their populations thus have the potential to rapidly adapt to these novel conditions.

Spatial and temporal evolution of heatwaves in Taiwan in a changing climate using multi-dimensional complementary ensemble empirical mode decomposition

Recent decades have witnessed noticeable warming in highly developed cities. Taiwan, a densely populated island characterized by its monsoon-dominated climate and unique mountainous topography, has experienced an average temperature increase of 1.0–1.4 °C in the last century, significantly exceeding the global average. This research introduces the Multi-dimensional Complementary Ensemble Empirical Mode Decomposition (MCEEMD) algorithm, providing a novel approach for analyzing gridded climate data and uncovering spatiotemporal temperature patterns in Taiwan. This methodology offers valuable insights into the localized impacts of climate change. The spatiotemporal evolution of temperature from 1960 to 2020 is analyzed to estimate the warming trend associated with climate change. Essential factors related to the urban heat island (UHI) effect, which leads to temperature disparities between urban and rural areas, are also examined. Warming and the UHI effect are critical components of climate change, increasing the likelihood of extreme weather events in urban areas. The study investigates extreme weather events from 1960 to 2020, focusing on heatwaves. It projects changes in the frequency and duration of heatwaves for the mid (2046–2065) and late 21st century (2081–2100). Notably, Taipei experienced a warming trend exceeding 1.5°C in the 2010s, which was likely influenced by urbanization. Additionally, areas experiencing the sharpest increase in heatwave events (more than 1.5 events) by 2000 were predominantly in highly developed cities across northern and southern Taiwan. Further analysis under the RCP8.5 scenario suggests that, by the end of the 21st century, heatwave events could last for at least 20 days in many regions, with the potential to extend to nearly a month during the summer.

Developing a statistical approach of evaluating daily maximum and minimum temperature observations from third‐party automatic weather stations in Australia

AbstractThird‐party automatic weather stations (TPAWS) provide a compelling data source for scientists and practitioners to observe and estimate more accurate fine‐scale atmospheric conditions, including daily maximum and minimum temperature (denoted as Tmax and Tmin, respectively), than the current primary weather observation network can offer. Several uncertainties and errors arise in data from TPAWS as the quality control applied to these stations may be inadequate or ad hoc. In this study, we develop a statistical approach to evaluate the quality of daily Tmax and Tmin observations collected from TPAWS in Australia. Our approach compares a target observation with multiple types of reliable reference data, including neighbouring primary weather observations from the official Bureau of Meteorology of Australia stations, Australian Gridded Climate Data, and numerical weather prediction data. Guided by the operational requirements in terms of automation, interpretability, and simplicity as well as expandability, a separate test is formed for each type of reference data and then all the individual tests are merged to generate a single result based on a Gaussian mixture model that is used to provide the final overall assessment for each TPAWS observation. The overall assessment is made in the form of a p‐value‐based confidence score that measures the difference between the target observation and trusted reference data. Our method is validated by synthetic datasets based on high‐quality observations and is also applied to daily Tmax and Tmin observations from 184 TPAWS owned by the Department of Primary Industries and Regional Development of Western Australia. The framework can be readily applied to different regions with different reliable or trusted data sources.

Current and future distribution of Eucalyptus globulus under changing climate in Ethiopia: implications for forest management

Eucalyptus globulus is a species endemic to southeastern Australia. It has naturalized non-native ranges in other parts of Australia, Europe, Africa, and the western United States. This study is the first of its kind in Ethiopia to model and map the spatiotemporal distribution of the species using species distribution models (SDMs). A total of 874 occurrence records were used from the online Global Biodiversity Information Facility (GBIF) database and field observation. Three environmental variables, including terrain, climate, and soil were used to predict the species’ distribution. The terrain, climate, and soil raster grids were resampled to a 200-meter resolution. The Global Circulation Model (GCM) HadGEM3-GC3.1 was used to extract future climate data. This GCM has a good match between the atmospheric and oceanic components showing little drift in its surface climate. Besides, it has the best coverage of Africa. Three climate change scenarios (SSPs 1-2.6, SSPs 2-4.5, and SSPs 5-8.5) were used for predicting suitable habitat of the species. The jackknife test was chosen to assess the importance of each environmental predictor variable. The model’s performance was evaluated using the Area under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The model had excellent predictive performance with an average AUC of 0.94. Altitude, rooting conditions, slope, dry-month precipitation, and temperature seasonality are the most important environmental factors in shaping E. globulus distribution. Ethiopian highlands are predicted to be more suitable to the species, but the increase in temperature seasonality may reduce suitable habitat under the high-forcing climate change scenario. Climate change is expected to create more suitable habitats for eucalyptus in the future which may encourage plantations in potential distribution areas. Consequently, ensuring long-term forest health necessitates robust management systems prioritizing native trees and responsible grower or farmer practices.

Selecting the optimal gridded climate dataset for Nigeria using advanced time series similarity algorithms.

Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.