Fifteen research needs for understanding climate change impacts on ecosystems and society in the Norwegian High North

There is an urgent need to understand and address the risks associated with a warming climate for ecosystems and societies in the Arctic and sub-Arctic regions. There are major gaps in our understanding of the complex effects of climate change-including extreme events, cascading impacts across ecosystems, and the underlying socioecological dynamics and feedbacks-all of which need collaborative efforts to be resolved. Here, we present results where climate scientists, ecologists, social scientists, and practitioners were asked to identify the most urgent research needs for understanding climate change impacts and to identify the actions for reducing future risks in catchment areas in the Norwegian High North, a region that encompasses both Arctic and sub-Arctic climates in northern Norway. From a list of 77 questions, our panel of 19 scientists and practitioners identified 15 research needs that should be urgently addressed. We particularly urge researchers to investigate cross-ecosystem impacts and the socioecological feedbacks that could amplify or reduce risks for society.

Selection of Representative General Circulation Models for Climate Change Study Using Advanced Envelope-Based and Past Performance Approach on Transboundary River Basin, a Case of Upper Blue Nile Basin, Ethiopia

For the selection of global climate models in the upper basin of the Blue Nile, an advanced envelope-based approach was used. Currently, the number of general circulations models (GCM) has increased extremely. The reliability of any general circulation model in a particular region is confronted, so the selection of the appropriate climate models that can predict the climate variable is essential. Representative concentration pathways RCP4.5 and RCP8.5 were taken into account. For RCP4.5 105 GCMs were used and for RCP8.5 78 GCMs were used to select the best performance models for the Upper Blue Nile Basin for a climate change impact study. Three steps were followed to derive the best performing models in the study area based on their range of projected mean temperature and precipitation changes, the range of projected extreme changes, and the ability to reproduce past climates between 1971 and 2000 and 2071–2100. Five corners of the spectrum were used, e.g., wet-warm, wet-cold, dry-warm, dry-cold, and the 50th percentile of the temperatures. For RCP4.5 and RCP8.5, a total of 25 GCMs were chosen based on the range of anticipated mean temperature and rainfall change. Based on the range of extreme changes, 10 GCMs were chosen. Finally, for each RCP4.5 and RCP8.5, five GCMs were chosen by combining all three stages.

Evaluating the Applicability of a Quantile–Quantile Adjustment Approach for Downscaling Monthly GCM Projections to Site Scale over the Qinghai-Tibet Plateau

In the context of global climate change, the Qinghai-Tibetan plateau (QTP) has experienced unprecedented changes in its local climate. While general circulation models (GCM) are able to forecast global-scale future climate change trends, further work needs to be done to develop techniques to apply GCM-predicted trends at site scale to facilitate local ecohydrological response studies. Given the QTP’s unique altitude-controlled climate pattern, the applicability of the quantile–quantile (Q-Q) adjustment approach for this purpose remains largely unknown and warrants investigation. In this study, this approach was evaluated at 36 sites to ensure the results are representative of different climatic and surface conditions on the QTP. Considering the practical needs of QTP studies, the study aims to assess its capability for downscaling monthly GCM simulations of major variables onto the site scale, including precipitation, air temperature, wind speed, relative humidity, and air pressure, based on two GCMs. The calibrated projections at the sites were verified against the observations and compared with those from two commonly used adjustment methods—the quantile-mapping method and the delta method. The results show that the general trends of most variables considered are well adjusted at all sites, with a quantile pair of 25–75% for all the variables except precipitation where 10–90% is used. The calibrated results are generally close to the observed values, with the best performance in air pressure, followed by air temperature and relative humidity. The performance is relatively limited in adjusting wind speed and precipitation. The accuracies decline as the adjustment extends into the future; a wider adjustment window may help increase the performance for the variables subject to climate changes. It is found that the performance of the adjustment is generally independent of the locations and seasons, but is strongly determined by the quality of GCM simulations. The Q-Q adjustment works better for the meteorological variables with fewer fluctuations and daily extremes. Variables with more similarities in probability density functions between the observations and GCM simulations tend to perform better in adjustment. Generally, this approach outperforms the two peer methods with broader applicability and higher accuracies for most major variables.

Dynamical Downscaling of ERA5 Data on the North-Western Mediterranean Sea: From Atmosphere to High-Resolution Coastal Wave Climate

A 29-year wind/wave hindcast is produced over the Mediterranean Sea for the period 1990–2018. The dataset is obtained by downscaling the ERA5 global atmospheric reanalyses, which provide the initial and boundary conditions for a numerical chain based on limited-area weather and wave models: the BOLAM, MOLOCH and WaveWatch III (WW3) models. In the WW3 computational domain, an unstructured mesh is used. The variable resolutions reach up to 500 m along the coasts of the Ligurian and Tyrrhenian seas (Italy), the main objects of the study. The wind/wave hindcast is validated using observations from coastal weather stations and buoys. The wind validation provides velocity correlations between 0.45 and 0.76, while significant wave height correlations are much higher—between 0.89 and 0.96. The results are also compared to the original low-resolution ERA5 dataset, based on assimilated models. The comparison shows that the downscaling improves the hindcast reliability, particularly in the coastal regions, and especially with regard to wind and wave directions.

The Impacts of Different Climate Change Scenarios on Visits toward the National Forest Park in Taiwan

Many studies have shown that the weather greatly affects the tourist count. Understanding weather information, climate change, and how they influence the tourist count in different tourist seasons (peak season, second peak season, off season) can help park planners and managers to analyze the opportunities and risks caused by climate change. This study aimed to predict the visitor count through information on the number of visitors and the weather day for three tourist seasons in a 12-month period. The study was conducted in the Huisun Forest Park of Taiwan based on the peak season (February, July, August, and October), the second peak season (January, April, May, November, and December), and the off-season (March, June, and September), using weather factors and virtual factors (such as whether it is a weekend or not) to establish three multivariate regression models for predicting the daily visitor count. This study assessed the impact of climate change on the visitor count and analyzed possible scenarios of climate change using representative concentration pathways (RCPs), as stated in the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC). The results of this study indicated that the impacts of weather factors on the visitor count is the same for the peak season and the off season. The temperature and relative humidity have a significant impact on the visitor count, and precipitation is not significant. In the second peak season, only the temperature has a significant impact on the visitor count. The relative humidity and precipitation are not significant. The temperature is the most influential factor in all three seasons, and has the highest influence on the peak season, followed by the low season, and then the second peak season. In addition, the number of visitors in Huisun Forest Park is on the rise, according to an analysis of various climate change scenarios (RCP2.6, RCP4.5, RCP6.0, RCP8.5). The results of this study can be used as a reference by forest park managers and future researchers. It is noted that the results were based on the current economic and political situation. The worsening of the entire world situation could break the relationships.

Analysis of Extreme Temperature Events over the Iberian Peninsula during the 21st Century Using Dynamic Climate Projections Chosen Using Max-Stable Processes

Due to climate change, the Iberian Peninsula is suffering an increasing trend of extreme temperature events. The main objective of this study is to analyse this trends in frequency, duration and intensity of warm events and heat waves. The datasets used are 14 different regionalized dynamic climate projections. We choose the projections that present a spatial dependence similar to that of observed data. The spatial dependence is calculated by adjusting the data to max-stable processes. The observed data belong to the SPAIN02 grid for the period 1961–2000. We apply the Mann-Kendall test and the Theil-Sen estimator to calculate model trends in the future period (2011–2099). We have studied future extreme temperature events using two different definitions. One varying the threshold for each period and the other keeping it constant. The results show that the variability of maximum temperatures is decreasing for the western region of the Peninsula, while the Mediterranean area will see an increase in this variability. There will be an increase in the frequency of warm events for the southwestern corner of the Peninsula. Also, maximum temperatures will be higher in this area at the end of the century. However, in the Mediterranean region the warm events will last longer. Heat waves will be more frequent throughout the territory and more lasting in the Mediterranean area. We also found that studying extreme events using a varying threshold allows these events to be studied from the point of view of the variability of maximum temperatures, while if the study is carried out maintaining the threshold constant the results will be more direct.

Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa

This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March–May (MAM) and October–December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.

Modified Approach to Reduce GCM Bias in Downscaled Precipitation: A Study in Ganga River Basin

Reanalysis data is widely used to develop predictor-predictand models, which are further used to downscale coarse gridded general circulation models (GCM) data at a local scale. However, large variability in the downscaled product using different GCMs is still a big challenge. The first objective of this study was to assess the performance of reanalysis data to downscale precipitation using different GCMs. High bias in downscaled precipitation was observed using different GCMs, so a different downscaling approach is proposed in which historical data of GCM was used to develop a predictor-predictand model. The earlier approach is termed “Re-Obs” and the proposed approach as “GCM-Obs”. Both models were assessed using mathematical derivation and generated synthetic series. The intermodal bias in different GCMs downscaled precipitation using Re-Obs and GCM-Obs model was also checked. Coupled Model Inter-comparison Project-5 (CMIP5) data of ten different GCMs was used to downscale precipitation in different urbanized, rural, and forest regions in the Ganga river basin. Different measures were used to represent the relative performances of one downscaling approach over other approach in terms of closeness of downscaled precipitation with observed precipitation and reduction of bias using different GCMs. The effect of GCM spatial resolution in downscaling was also checked. The model performance, convergence, and skill score were computed to assess the ability of GCM-Obs and Re-Obs models. The proposed GCM-Obs model was found better than Re-Obs model to statistically downscale GCM. It was observed that GCM-Obs model was able to reduce GCM-Observed and GCM-GCM bias in the downscaled precipitation in the Ganga river basin.

Lessons from Inter-Comparison of Decadal Climate Simulations and Observations for the Midwest U.S. and Great Lakes Region

Even with advances in climate modeling, meteorological impact assessment remains elusive, and decision-makers are forced to operate with potentially malinformed predictions. In this article, we investigate the dependence of the Weather Research and Forecasting (WRF) model simulated precipitation and temperature at 12- and 4-km horizontal resolutions and compare it with 32-km NARR data and 1/16th-degree gridded observations for the Midwest U.S. and Great Lakes region from 1991 to 2000. We used daily climatology, inter-annual variability, percentile, and dry days as metrics for inter-comparison for precipitation. We also calculated the summer and winter daily seasonal minimum, maximum, and average temperature to delineate the temperature trends. Results showed that NARR data is a useful precipitation product for mean warm season and summer climatological studies, but performs extremely poorly for winter and cold seasons for this region. WRF model simulations at 12- and 4-km horizontal resolutions were able to capture the lake-effect precipitation successfully when driven by observed lake surface temperatures. Simulations at 4-km showed negative bias in capturing precipitation without convective parameterization but captured the number of dry days and 99th percentile precipitation extremes well. Overall, our study cautions against hastily pushing for increasingly higher resolution in climate studies, and highlights the need for the careful selection of large-scale boundary forcing data.

Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB)

This study focusses on identifying a set of representative climate model projections for the Upper Indus Basin (UIB). Although a large number of General Circulation Models (GCM) predictor sets are available nowadays in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for the step-wise shortlisting and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on: (a) range of projected mean changes, (b) range of projected extreme changes, and (c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, from of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to the year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range from a slight annual increase of 2.2% under the drier scenarios to as high as 15.9% in the wet scenarios. Moreover, for all scenarios, future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the “Dry-Warm” and the “Median” scenarios over the late 21st century.

Redefining thermal regimes to design reserves for coral reefs in the face of climate change

Reef managers cannot fight global warming through mitigation at local scale, but they can use information on thermal patterns to plan for reserve networks that maximize the probability of persistence of their reef system. Here we assess previous methods for the design of reserves for climate change and present a new approach to prioritize areas for conservation that leverages the most desirable properties of previous approaches. The new method moves the science of reserve design for climate change a step forwards by: (1) recognizing the role of seasonal acclimation in increasing the limits of environmental tolerance of corals and ameliorating the bleaching response; (2) using the best proxy for acclimatization currently available; (3) including information from several bleaching events, which frequency is likely to increase in the future; (4) assessing relevant variability at country scales, where most management plans are carried out. We demonstrate the method in Honduras, where a reassessment of the marine spatial plan is in progress.

Changing human landscapes under a changing climate: considerations for climate assessments

Climate change is a fundamental aspect of the Anthropocene. Climate assessments are frequently undertaken to evaluate climate change impacts, vulnerability, and adaptive capacity. Assessments are complex endeavors with numerous challenges. Five aspects of a climate assessment that can be particularly challenging are highlighted: choice of assessment strategy, incorporation of spatial linkages and interactions, the constraints of climate observations, interpretation of a climate projection ensemble, uncertainty associated with weather/climate dependency models, and consideration of landscape-climate influences. In addition, a climate assessment strategy that incorporates both traditional "top-down" and "bottom-up" methods is proposed for assessments of adaptation options at the local/regional scale. Uncertainties associated with climate observations and projections and with weather/climate dependency (i.e., response) models are incorporated into the assessment through the "top-down" component, and stakeholder knowledge and experience are included through the "bottom-up" component. Considerable further research is required to improve assessment strategies and the usefulness and usability of assessment findings. In particular, new methods are needed which better incorporate spatial linkages and interactions, yet maintain the fine grain detail needed for decision making at the local and regional scales. Also, new methods are needed which go beyond sensitivity analyses of the relative contribution of land use and land cover changes on local/regional climate to more explicitly consider landscape-climate interactions in the context of uncertain future climates. Assessment teams must clearly communicate the choices made when designing an assessment and recognize the implications of these choices on the interpretation and application of the assessment findings.