Resolution Climate Data Research Articles

Impact of Soil Moisture Dynamics and Precipitation Pattern on UK Urban Pluvial Flood Hazards Under Climate Change

AbstractThe diversity of flood‐generating mechanisms superimposed on catchment physiographic features with non‐stationary meteorological drivers makes future flood hazard assessment a grand challenge. To date, many studies have examined patterns in rainfall and streamflow, but far fewer have investigated trends in the other drivers of flooding. The complex transfer function between precipitation and flooding makes it potentially misleading to simply look at the change in rainfall to express the hazard. Furthermore, there are very few studies that have directly used output from km‐scale climate models in flood modeling. Coarse resolution climate data sets may not credibly resolve local climate and weather extremes. Changes in rainfall distribution and antecedent moisture over extended time periods due to climate change have so far been ignored when assessing urban pluvial flood risk. In this paper, an urban flood hazard assessment framework using the latest 2.2 km resolution UK Climate Projections Local is proposed. Global warming induced changes in pluvial flood risks under RCP8.5 are projected, focusing on the impact of changing precipitation patterns and soil moisture dynamics on flood generation. Results indicate a strong increase in the frequency of occurrence of extreme floods, and the resultant future (2060–2080) annual flood volume is expected to increase up to 52.6% relative to 1980–2000 over a major UK urban region, and these patterns are likely to hold more generally elsewhere in the UK. Shifts to a later occurrence of extreme flooding is identified under global warming. Previous studies that have neglected soil moisture dynamics are unlikely to give accurate flood estimates.

Downscaling air temperatures for high-resolution niche modeling in a valley of the Amazon lowland forests: A case study on the microclima R package.

The forests of the Amazon basin are threatened by climate and land use changes. Due to the transition towards a drier climate, moisture-dependent organisms such as canopy epiphytes are particularly affected. Even if the topography in the Amazon lowland is moderate, mesoscale nocturnal katabatic flows result from cold air production related to radiative cooling. From a certain level of mass the cold air starts to flow downslope towards the valley centers leading to temperature inversions. The resulting cooling in the valleys drives localized fog formation in the valleys at night. This correlates with high epiphyte abundance and diversity in the valleys, which is much less pronounced upslope. The underlying temperature dynamics are, however, not sufficiently included in coarse-resolution reanalysis models such as ERA5-Land. Since high resolution climate data are needed e.g. for proper niche modeling of locally distributed species such as canopy epiphytes, downscaling models such as microclima have been developed and include micro- and mesoscale effects. However, it is unclear how well the elevation-related diurnal course of air temperature can be simulated. Here, we test functions for downscaling coarse-resolution temperature data to high spatial resolution data implemented in the R-package microclima for the South American tropical lowland forests. To do so we compared microclima-downscaled ERA5-Land air temperature data with meteorological station data. We found that the microclima functions only properly detect 73 temperature inversions out of 412 nocturnal cold air drainage (CAD) events during the dry season study period and only 18 out of 400 during the wet season with default settings. By modifying default values such as the emissivity threshold and time frames of possible CAD condition detection, we found 345 of 412 CAD events during the dry season and 177 out of 400 during the wet season. Despite problems with the distinction between CAD and non-CAD events the microclima algorithms show difficulties in correctly modeling the diurnal course of the temperature data and the amplitudes of elevational temperature gradients. For future studies focusing on temperature downscaling approaches, the modules implemented in the microclima package have to be adjusted for their usage in tropical lowland forest studies and beyond.

Spatiotemporal climate and vegetation trends, and their relationship: A case of Genale Dawa basin, Ethiopia

Understanding the variability of climate and vegetation, and their relationship in space and time are crucial for agricultural planning, flood risk assessment, and drought monitoring. However, most studies depend on scarce and unevenly distributed climate station datasets in most basins of Ethiopia like the Genale Dawa basin. Detailed climate and vegetation variabilities and their relationship have not well studied in the Genale Dawa basin, which is often affected by recurrent drought. Satellite remote sensing-derived high spatial resolution climate and vegetation data can provide detailed information on climate variability and vegetation changes. This study assessed spatiotemporal variability of temperature, rainfall, and vegetation greenness and their relationship in the Genale Dawa basin. For this study, temperature (minimum and maximum), rainfall data (4 × 4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (250 × 250 m) were used. Statistical indices like the Mann Kendall (MK) trend test, Coefficient of Variation (CV), Standard Anomaly Index (SAI), Precipitations Concentration Index (PCI), and Pearson correlation techniques were used to examine the trends and variabilities. The result generally indicated increasing trends in rainfall during the dry (“Bega”) season, and maximum and minimum temperatures during Kiremt (Wet season) and annual seasons. Moreover, high rainfall variability in the Dry and Kiremt seasons and low variability of temperature at all timescales were observed in the basin. In addition, anomalies in temperatures (minimum and maximum) and rainfall were observed in the basin during all the timescales. The result further showed a strong correlation of NDVI with rainfall at annual (r = 0.58), dry season (r = 0.9), and the short rainy season (r = 0.7) while weakly correlated at Kiremt (r = 0.31) seasons in the Genale Dawa basin. The result generally showed spatial and temporal variability of climate and vegetation greenness in the Genale Dawa basin, which negatively affects agricultural production, water resource availability, and vegetation growth. Therefore, appropriate management strategies should be implemented to minimize the negative effects.

Do farmers’ risk preferences remain stable in the face of climatic shocks? Evidence from smallholder farmers in Zambia

ABSTRACT Smallholder farmers in Africa are vulnerable to changing climatic conditions because of their dependence on rainfed agriculture. Their adaptation strategies, which are critical for food security, are oftentimes influenced by their risk-taking attitude. This study examines whether experiencing climatic shocks (defined here as drought events) shapes farmers’ risk aversion. The study addresses two crucial questions: 1) how do drought events alter risk preferences among smallholder farmers; and 2) how long does the impact last? Using a panel survey from Zambia and high spatial resolution climate data, we infer the average Arrow-Pratt and downside risk aversion coefficients from the moment of the distribution of crop production. We find that, on average, the sampled farmers are risk averse. Furthermore, farmers who experienced a drought in the previous year become more risk averse, while farmers who experienced recurring droughts within the previous three years become even more risk averse. We also find that the effect of climatic shocks on risk preferences is short term, lasting only one year. These results have implications on adaptation to climate change.

Using LASSO regularization to project recruitment under CMIP6 climate scenarios in a coastal fishery with spatial oceanographic gradients

As climate change disrupts fisheries, scientists are interested in fisheries projections under climate change scenarios. However, projections that account for spatial oceanographic gradients use increased variable selection power and output high spatial resolution climate data are needed to improve strategic fisheries management. This study uses the least absolute squares and selection operator, a regularization technique, and improved, climate change projections from phase 6 of the Couple Model Intercomparison Project to relate Atlantic surfclam, Spisula solidissima solidissima, recruitment to climate variables. Results show a longitudinal gradient in New York State waters where western recruitment displays a negative relationship with sea surface temperature and eastern recruitment displays a negative relationship with eastward spring wind intensity. Models project that recruitment in 2050 will decrease 100% in western waters and remain sporadic, but high, in eastern waters. This study provides insight regarding surfclam responses to climate change and considerations (methodological and statistical) for improved climate-based fisheries projections.

A 20-year vegetation cover change and its response to climate factors in the Guangdong-Hong Kong-Macao Greater Bay Area under the background of climate change

IntroductionThe Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is located in the south subtropical area along the southeast coast of China, which is one of the world-class urban agglomerations and an important part for economic development. In order to investigate the change of vegetation indexes and its response to climate factors in such circumstance of climate change, this study is an important component in the protection and establishment of the ecological environment in the GBA.MethodsThe Moderate Resolution Imaging Spectroradiometer-Enhanced Vegetation Index (MODIS-EVI) and climate data were recorded from National Aeronautics and Space Administration (NASA) and Resource and Environment Science Data Center of the Chinese Academy of Sciences. Trend analysis, Mann-Kendall (MK) Test and rescaled range analysis (R/S Analysis) offer an effective way of analyzing the correlation between the vegetation cover change and climate factors.ResultsThe results provide important insights into the following aspects: (1) The changes of climate factors (temperature, precipitation, wind speed, humidity, and sunshine radiation) are fluctuated in GBA, with no obvious increasing or decreasing trend. It comprehensively exhibited an extremely slow development of humidify and warming. (2) It presents an increasing trend of EVI in GBA, with the rate of 0.0045/a. The range of increase is in the middle level (0.4 ≤ EVI<0.6) based on the EVI. The vegetation cover in GBA is improved comprehensively, the area of vegetation improvement is larger than the area of vegetation degression, with the extremely improved vegetation cover area (66.98%) and the extremely degraded vegetation cover area (5.70%). There are obvious differences and agglomerations in the distribution of the EVI trends. (3) In future, the changing trends will be combinedly affected be various factors, and there is no obvious factor temporarily. The improved vegetation cover area (over 80%) are predicted. (4) There are significant spatiotemporal differences in the annual effects of EVI on various climate factors comprehensively. Wind speed and relative humidity have the strongest correlations with EVI; the area of significant correlation is more than 40% of the pixels. The correlation between temperature and EVI is second, with the area of significant correlation over 20% of the pixels. The precipitation and sunshine radiation weakly correlated with EVI, with the area of significant correlation is less than 5% of the pixels.DiscussionThe result of this study indicated that the EVI changing trend in the future by R/S analysis method is affected by climate and human factors together and there are no significant factors. The result indicated precipitation has no significant correlation with EVI trends in the Hot and humid area with mean precipitation of 1800mm. However, there is a significant positive correlation between the EVI trend and two climate factors (relative humidity and wind speed). In the terms of spatial distribution, the influence of temperature to EVI is complex in GBA, the spatial distribution of correlation is scattered.

A simple hybrid statistical–dynamical downscaling method for emulating regional climate models over Western Europe. Evaluation, application, and role of added value?

A hybrid statistical dynamical downscaling method intended to emulate regional climate models is described and applied to Western Europe. The method is based on a constructed analogues algorithm, already used for statistical downscaling. For emulation, the statistical downscaling relationship is not derived from observations but from climate projections at low and high resolution. The hybrid approach therefore does not rely on the stationarity assumption inherent to conventional statistical downscaling. Within a perfect model framework, and using a large number of regional projections, the hybrid method is shown to reproduce climate change signals very well and to outperform a conventional statistical downscaling method also based on constructed analogues. The hybrid approach remains skillful even when applied to very low resolution climate data. In practice, two emulation modes exist. In the GCM/RCM mode, the downscaling relationship is built between a RCM and its forcing GCM. In the RCM/RCM mode, the relationship is built between a RCM and the same RCM after aggregation of its results to a low resolution grid. The large-scale climate change signal of the downscaled GCM is generally retained with the RCM/RCM mode, but not with the GCM /RCM mode. Additionally, the choice of the GCM/RCM pair used for learning leads to large differences in downscaling results at large scale (i.e. at low resolution) with the GCM /RCM mode, but not with the RCM/RCM mode. These results are explained by the differences that generally exist at large scale between projected changes by current RCMs and their forcing GCMs. Whether these differences are a testimony of a real added value of RCMs at large scale in the climate change context, or whether they have other causes, is therefore a crucial question.

Estimating phenological sensitivity in contemporary vs. historical data sets: Effects of climate resolution and spatial scale.

Phenological sensitivity, or the degree to which a species' phenology shifts in response to warming, is an important parameter for comparing and predicting species' responses to climate change. Phenological sensitivity is often measured using herbarium specimens or local studies in natural populations. These approaches differ widely in spatiotemporal scales, yet few studies explicitly consider effects of the geographic extent and resolution of climate data when comparing phenological sensitivities quantified from different data sets for a given species. We compared sensitivity of flowering phenology to growing degree days of the alpine plant Silene acaulis using two data sets: herbarium specimens and a 6 yr observational study in four populations at Niwot Ridge, Colorado, USA. We investigated differences in phenological sensitivity obtained using variable spatial scales and climate data sources. Herbarium specimens underestimated phenological sensitivity compared to observational data, even when herbarium samples were limited geographically or to nearby weather station data. However, when observational data were paired with broader-scale climate data, as is typically used in herbarium data sets, estimates of phenological sensitivity were more similar. This study highlights the potential for variation in data source, geographic scale, and accuracy of macroclimate data to produce very different estimates of phenological responses to climate change. Accurately predicting phenological shifts would benefit from comparisons between methods that estimate climate variables and phenological sensitivity over a variety of spatial scales.

Investigating the potential for Southern Hemisphere climate reconstruction using stable isotopes in Tasmanian tree rings

Few annually dated stable isotope records exist across Oceania. Stable carbon and oxygen isotope ratios have the potential to enhance climate reconstructions currently reliant on tree ring width chronologies. The purpose of this study is to explore the sources of variability in a stable oxygen isotope chronology derived from A. selaginoides from Mount Read, Tasmania. This high elevation site receives abundant rainfall throughout the year and is ∼130 km from the Global Network of Isotopes in Precipitation (GNIP) site at Cape Grim. We crossdated 10 new tree core samples against an existing ring width chronology (954–2011 CE) and analyzed the δ18O from the individual rings for the period 1960–2018. Using high resolution (0.25 degrees) climate data and ECMWF ERA5 reanalysis data, we disentangled the effects of local climate and source region on the isotopic signatures recorded in the annual rings. In addition, we used HYSPLIT backward trajectory analysis to characterize the source region of precipitation to Mount Read and whether the source region has influence over the δ18OTR series. Median δ18OTR (n = 10) is correlated with local temperature and vapor pressure deficit in the early growing season. In addition, spatial correlations reveal that median δ18OTR is positively correlated with temperature and negatively correlated with precipitation in the source region. However, measurements of δ18OTR exhibit high inter-tree variation, particularly between 1960 and 1990. Our results indicate that this δ18OTR proxy may provide additional information about past moisture conditions during the growing season, potentially contributing to more robust reconstructions of the Southern Hemisphere climate dynamics; however, additional sampling may be necessary to resolve inter-tree variation in δ18OTR.

Simulating the potential forest ranges in an old land: the case for Turkey’s forests

The central aim of this study is to estimate the current potential distribution of Turkey’s forests and their composition in absence of anthropogenic landcover change, and in this manner to contribute to past studies on the distribution of European forest taxa by filling an important spatial gap. For our simulations, we used high resolution climate data from ERA5-Land and a process based dynamic regional to global vegetation model (LPJ-GUESS), for which we modified the original European tree species list to match the climatic sensitivities that the species in turn exhibit throughout their natural ranges in Turkey, alternating between subspecies where necessary. This is the first application of LPJ-GUESS for the entire country using high resolution climate data. We defined the climatic tolerances for the model of three conifer species with wide distribution in Turkey: Pinus nigra, Pinus brutia, and Cedrus libani. We first identified relative bioclimatic tolerances of species for Turkey by way of comparing their current recorded distributions to ERA5-Land reanalysis data using observation data from the General Directorate of Forestry of Turkey, and later through ensemble runs with LPJ-GUESS we finetuned their parameters. Finally, we compared simulated net primary production to MODIS remote sensing observation estimates. In general, LPJ-GUESS captured the natural distribution ranges of different forest taxa, especially after the introduction of three additional species native to Turkey, highlighting competition for resources. Due to probable regional acclimatization of species, our final set of parameters for the different plant functional types offers a suitable alternative for future studies.

How agricultural droughts are contributing to child undernutrition in sub-Saharan Africa

Countries in sub-Saharan Africa (SSA) have some of the highest levels of child malnutrition, with more than one third of children under five in the region characterized as chronically undernourished. High reliance on subsistence farming, poor adoption of irrigation technologies and variable climate conditions make populations in SSA highly vulnerable to malnutrition during droughts. We use anthropometric data for 570,540 children under the age of five from 34 countries in SSA in combination with high resolution agricultural and climate data to estimate the association between agricultural droughts and child undernutrition in the region. We use global gridded data on the geographical distribution of crop areas for 19 major crops. Data on crop planting and harvesting dates are also collected for each crop. The Standardized Precipitation Evapotranspiration Index (SPEI), a multi-scalar drought index, is used to measure the intensity and spatial distribution of droughts during key periods of agricultural production (planting, growth and harvesting) and of different duration (seasonal and long-lasting droughts). Our preliminary analysis shows that droughts during the main crop growing season are associated with increased risk of child undernutrition in SSA. As a next step, we will quantify the impact of droughts during the planting and harvesting seasons and for specific crops. We will additionally investigate whether droughts impact pastoral communities, for example through livestock loss. The findings presented in this study call for urgent action to improve drought monitoring and response in SSA where the risks to child health posed by global warming are considerable. Under climate change, the severity and frequency of extreme weather and climate events, including droughts, are projected to increase in the future, which will place millions of children at risk of hunger unless timely actions plans are taken to improve food security in the region. Key words: drought, undernutrition, agriculture, sub-Saharan Africa

Assessment of wind and photovoltaic power potential in China

Decarbonization of the energy system is the key to China’s goal of achieving carbon neutrality by 2060. However, the potential of wind and photovoltaic (PV) to power China remains unclear, hindering the holistic layout of the renewable energy development plan. Here, we used the wind and PV power generation potential assessment system based on the Geographic Information Systems (GIS) method to investigate the wind and PV power generation potential in China. Firstly, the high spatial-temporal resolution climate data and the mainstream wind turbines and PV modules, were used to assess the theoretical wind and PV power generation. Then, the technical, policy and economic (i.e., theoretical power generation) constraints for wind and PV energy development were comprehensively considered to evaluate the wind and solar PV power generation potential of China in 2020. The results showed that, under the current technological level, the wind and PV installed capacity potential of China is about 56.55 billion kW, which is approximately 9 times of those required under the carbon neutral scenario. The wind and PV power generation potential of China is about 95.84 PWh, which is approximately 13 times the electricity demand of China in 2020. The rich areas of wind power generation are mainly distributed in the western, northern, and coastal provinces of China. While the rich areas of PV power generation are mainly distributed in western and northern China. Besides, the degree of tapping wind and PV potential in China is not high, and the installed capacity of most provinces in China accounted for no more than 1% of the capacity potential, especially in the wind and PV potential-rich areas.

Evaluation of WRF Cumulus Parameterization Schemes for the Hot Climate of Sudan Emphasizing Crop Growing Seasons

High spatiotemporal resolution climate data are essential for climate-related impact studies. The Weather Research and Forecasting (WRF) model is widely used to downscale climate data for different regions with regional-specific physics configurations. This study aimed to identify robust configurations of the WRF model, especially cumulus parameterization schemes, for different climatic zones of Sudan. We focused on wet season (June–September) rainfall and dry season (November–February) temperature, which are determinants of summer crop and irrigated wheat yields, respectively. Downscaling experiments were carried out to compare the following schemes: Betts–Miller–Janjic (BMJ), improved Kain–Fritch (KFT), modified Tiedtke (TDK), and Grell–Freitas (GF). Results revealed that the BMJ performed better for wet season rainfall in the hyper-arid and arid zones; KFT performed better for rainfall in July and August in the semi-arid zone where most summer crops are cultivated. For dry season temperature, the BMJ and TDK outperformed the other schemes in all three zones, except that the GF performed best for the minimum temperature in December and January in the arid zone, where irrigated wheat is produced, and in the semi-arid zone. Specific parameterization schemes therefore need to be selected for specific seasons and climatic zones of Sudan.

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

AbstractTropical montane cloud forest may be especially sensitive to climate change. However, our ability to understand effects of climate on montane biodiversity remains limited by the resolution of climate data. We compared 5 years of in situ weather data from cloud forests in northern Peru, regional weather stations, and gridded datasets to examine how climatologies reflect (a) forest microclimate buffering and (b) local rainfall in a sparse data region; we also examined spatiotemporal variability and regional trends. Across a 1,700–3,100 m gradient in which temperature did not covary with relative humidity (RH), in situ data showed interactions between climate and land‐use. Forest humidity buffered warming‐induced evaporative drying across elevations, and inside forest maximum vapour pressure deficit (VPDmax) did not change with elevation, whereas with a 22% reduction in RHmin at stations, VPDmax increased >10‐fold from high to low elevations. Cloud forest dried out on sunny days after 3 days without rain, especially during ENSO‐related drought concurrent with peak solar insolation. Climatologies were twice as precise for temperature as rainfall. Chelsa captured a 3.9°C reduction in maximum temperatures inside forest (MAE 1.6°C, R2 = 0.95) whereas WorldClim reflected drier lapse rates and higher Tmax outside forest. CHIRPS provided the best fit for monthly rainfall (MAE 23 mm, R2 = 48), capturing regional drought but underestimating rainfall >150 mm·month−1. Consistent with stations, CHIRPS showed strong support for regional increases in wet‐season rainfall. Reduced variability and more regular dry seasons were only detected by montane stations, especially south of 6°S, where rainfall seasonality shifted to earlier wet‐season peaks and reduced dry‐season rainfall as part of a transition from the Northern to Central Andes. Our results show that cloud forests may be partly buffered from warming but are likely to become extremely vulnerable under reduced humidity either through forest loss or drought.

Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models

In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a process-based crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved in-season subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest.

Contributions to the Improvement of Climate Data Availability and Quality for Sub-Saharan Africa

Reliable weather observations are the basis to assess climate change and variability. Compared to other regions of the world, long time series of weather observations are sparse in many countries of Sub-Saharan Africa. Various activities at national or international level are ongoing to improve the availability and quality of climate databases. Here, we present ongoing international contributions with a focus on representative examples hosted at Germany's national meteorological service DWD (Deutscher Wetterdienst). The international exchange of monthly climate reports (CLIMAT) is monitored within the Monitoring Centre of the GCOS Surface Network (Global Climate Observing System). In that context also quality control is performed and data are made publicly available. Recent climate observations can be complemented by digitization of historical hand-written weather observations which are available in distributed archives. International data centers, such as the Global Precipitation Climatology Centre (GPCC), collect international data. They perform quality-control of these observations and provide derived products in support of global and regional climate assessments. These activities can also contribute to the improvement of national climate databases, as e.g., demonstrated in a cooperation among selected countries with the SASSCAL initiative (Southern African Science Service Centre for Climate Change and Adaptive Land Management). Satellite-based observations are an additional source that can provide climatological information for selected parameters. In particular, the METEOSAT satellite series provides valuable data for the African continent. The Satellite Application Facility on Climate Monitoring (CM SAF) provides high resolution climate data covering the last decades derived from observations of such meteorological satellites. Based on these examples the paper illustrates the variety of ongoing international efforts in support of regional observation-based climate information, but also identifies the needs for further activities.

Fossil Primate Teeth as an Archive for High Resolution Ontogenic, Dietary and Palaeoclimate Record

ABSTRACT Rhythmic incremental growth of daily or longer duration is faithfully recorded in the tooth enamel of all the mammals including those of primates. Just a single well preserved fossil primate tooth can yield a variety of invaluable information, ranging from gestation period, age at weaning, age at death, prenatal and postnatal diet, ecology, water intake, and even body and brain size of an individual, that lived millions of years ago. Till a few years back fossil teeth of rare primates were physically sectioned to count these rhythmic growth lines for extracting ontogenic information such as, crown formation times, daily secretion rates, rhetzius line periodicity and enamel extension rates. Now all this is changing with the application of non-invasive X-Ray Synchroton micro-tomography. Further, laser ablation based stable carbon and oxygen isotope analyses of these growth layers are showing immense potential for yielding high resolution dietary, ecological and climate data, with minimal visible damage to the very rare fossil specimens.

Uncertainty, Complexity and Constraints: How Do We Robustly Assess Biological Responses under a Rapidly Changing Climate?

How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for biological impact assessments and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative assessments. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate–ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop a more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.

Could Pine Wood Nematode (Bursaphelenchus xylophilus) Cause Pine Wilt Disease or Even Establish inside Healthy Trees in Finland Now—Or Ever?

Pine wilt disease (PWD) caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus) can, in suitable conditions, lead to mass mortality of susceptible trees. In the European Union, PWN is a quarantine pest. To support PWN risk management in Finland, we assessed the suitability of the Finnish present and future climate for both PWD and PWN establishment inside susceptible healthy trees. The former was done using the mean summer temperature concept and the latter by relating annual growing degree days to the likelihoods of PWN extinction and establishment inside healthy trees. The likelihoods were derived from the previously published modelling of PWN population dynamics for 139 locations in Germany. Both assessments were conducted using 10 × 10 km resolution climate data from 2000–2019 and Finland-specific climate change projections for 2030–2080. The results indicate that the present Finnish climate is too cool for both PWD and PWN establishment inside healthy trees. Furthermore, even global warming does not appear to turn the Finnish climate suitable for PWD or PWN establishment inside healthy trees by 2080, except under the worst-case representative concentration pathway scenario (RCP8.5). Consequently, giving top priority to PWN when allocating resources for biosecurity activities in Finland might deserve reconsideration.

Reconciling high resolution climate datasets using KrigR

There is an increasing need for high spatial and temporal resolution climate data for the wide community of researchers interested in climate change and its consequences. Currently, there is a large mismatch between the spatial resolutions of global climate model and reanalysis datasets (at best around 0.25° and 0.1° respectively) and the resolutions needed by many end-users of these datasets, which are typically on the scale of 30 arcseconds (∼900 m). This need for improved spatial resolution in climate datasets has motivated several groups to statistically downscale various combinations of observational or reanalysis datasets. However, the variety of downscaling methods and inputs used makes it difficult to reconcile the resultant differences between these high-resolution datasets. Here we make use of the KrigR R-package to statistically downscale the world-leading ERA5(-Land) reanalysis data using kriging. We show that kriging can accurately recover spatial heterogeneity of climate data given strong relationships with co-variates; that by preserving the uncertainty associated with the statistical downscaling, one can investigate and account for confidence in high-resolution climate data; and that the statistical uncertainty provided by KrigR can explain much of the difference between widely used high resolution climate datasets (CHELSA, TerraClimate, and WorldClim2) depending on variable, timescale, and region. This demonstrates the advantages of using KrigR to generate customized high spatial and/or temporal resolution climate data.