Regional Climate Change Projections Research Articles

Atmospheric Dynamics is the Largest Source of Uncertainty in Future Winter European Rainfall

Abstract The IPCC Fifth Assessment Report highlighted large uncertainty in European precipitation changes in the coming century. This paper investigates the sources of intermodel differences using CMIP5 model European precipitation data. The contribution of atmospheric circulation to differences in precipitation trends is investigated by applying cluster analysis to daily mean sea level pressure (MSLP) data. The resulting classification is used to reconstruct monthly precipitation time series, thereby isolating the component of precipitation variability directly related to atmospheric circulation. Reconstructed observed precipitation and reconstructions of simulated historical and projection data are well correlated with the original precipitation series, showing that circulation variability accounts for a substantial fraction of European precipitation variability. Removing the reconstructed precipitation from the original precipitation leaves a residual component related to noncirculation effects (and any small remaining circulation effects). Intermodel spread in residual future European precipitation trends is substantially reduced compared to the spread of the original precipitation trends. Uncertainty in future atmospheric circulation accounts for more than half of the intermodel variance in twenty-first-century precipitation trends for winter months for both northern and southern Europe. Furthermore, a substantial part of this variance is related to different forced dynamical responses in different models and is therefore potentially reducible. These results highlight the importance of understanding future changes in atmospheric dynamics in achieving more robust projections of regional climate change. Finally, the possible dynamical mechanisms that may drive the future differences in regional circulation and precipitation are illustrated by examining simulated teleconnections with tropical precipitation.

Analysis of Climatic Trends in Evaporation for Çanakkale (Turkey)

In this study, temporal changes and trends in the series of annual, seasonal, and monthly evaporation of Canakkale station of Turkish State Meteorological Service were analyzed. Time series of evaporation data set has been organized as climatological seasons that spring (March, April, May), summer (June, July, August), autumn (September, October, November), and winter (December, January, February). Non-parametric tests and Box-Jenkins method were used to determine climatic trends. Pettitt change-point analysis was applied to determine the change point of evaporation. Trend analysis results showed that a statistically significant increasing trend occurred in evaporation. Mean annual evaporation is estimated to increase 1.4498 mm per year and it is anticipated to reach 215.3356 mm in 2022. Furthermore, mean seasonal evaporation are estimated to increase 1.2251 mm, 1.6485 mm, and 0.4117 mm per year for spring, summer, and autumn, respectively. Therefore, Canakkale is thought to be affected by global warming and climate change and this effect will continue. Evaporation should be continuously measured and monitoring program should be established to allow sustainable use and management of water resources. Global or regional climate change scenarios and projections must be considered in order to moderate the possible effects of climate change and global warming on Canakkale.

Improving Regional Climate Projections by Prioritized Aggregation via Ordered Weighted Averaging Operators

Abstract Decision makers express a strong need for reliable information on future climate changes to develop with the best mitigation and adaptation strategies to address impacts. These decisions are based on future climate projections that are simulated by using different Representative Concentration Pathways (RCPs), General Circulation Models (GCMs), and downscaling techniques to obtain high-resolution Regional Climate Models. RCPs defined by the Intergovernmental Panel on Climate Change entail a certain combination of the underlying driving forces behind climate and land use/land cover changes, which leads to different anthropogenic Greenhouse Gases concentration trajectories. Projections of global and regional climate change should also take into account relevant sources of uncertainty and stakeholders' risk attitudes when defining climate polices. The goal of this article is to improve regional climate projections by their prioritized aggregation through the ordered weighted averaging (OWA) operator....

Inflated Uncertainty in Multimodel‐Based Regional Climate Projections

Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5 models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere.

Role of resolution in regional climate change projections over China

This paper investigates the sensitivity of projected future climate changes over China to the horizontal resolution of a regional climate model RegCM4.4 (RegCM), using RCP8.5 as an example. Model validation shows that RegCM performs better in reproducing the spatial distribution and magnitude of present-day temperature, precipitation and climate extremes than the driving global climate model HadGEM2-ES (HadGEM, at 1.875° × 1.25° degree resolution), but little difference is found between the simulations at 50 and 25 km resolutions. Comparison with observational data at different resolutions confirmed the added value of the RCM and finer model resolutions in better capturing the probability distribution of precipitation. However, HadGEM and RegCM at both resolutions project a similar pattern of significant future warming during both winter and summer, and a similar pattern of winter precipitation changes including dominant increase in most areas of northern China and little change or decrease in the southern part. Projected precipitation changes in summer diverge among the three models, especially over eastern China, with a general increase in HadGEM, little change in RegCM at 50 km, and a mix of increase and decrease in RegCM at 25 km resolution. Changes of temperature-related extremes (annual total number of daily maximum temperature > 25 °C, the maximum value of daily maximum temperature, the minimum value of daily minimum temperature in the three simulations especially in the two RegCM simulations are very similar to each other; so are the precipitation-related extremes (maximum consecutive dry days, maximum consecutive 5-day precipitation and extremely wet days’ total amount). Overall, results from this study indicate a very low sensitivity of projected changes in this region to model resolution. While fine resolution is critical for capturing the spatial variability of the control climate, it may not be as important for capturing the climate response to homogeneous forcing (in this case greenhouse gas concentration changes).

Global connectivity and cross-scale interactions create uncertainty for Blue Growth of Arctic fisheries

The Arctic faces high expectations of Blue Growth due to future projections of easier access and increased biological productivity. These expectations are, however, often based on global and regional climate change projections and largely ignore the complexity of social-ecological interactions taking place across different temporal and spatial scales. This paper illustrates how such cross-scale interactions at, and across, different dimensions (e.g., ecological, socioeconomic and governance) can affect the development of Arctic fisheries; and potentially create uncertainties for future Blue Growth projections. Two Arctic marine systems, The Barents Sea and the Central Arctic Ocean (CAO), are used as focus areas. The former hosts productive fisheries and is mostly covered by the EEZs of Norway and Russia, while the latter is still mainly covered by sea-ice and is a high seas area with no multilevel governance system in place. The examples show that, both systems are affected by a number of processes, beyond the environmental change, spanning a wide range of dimensions, as well as spatial and temporal scales. To address the complexity of the Arctic marine systems calls for an increase in holistic scientific understanding together with adaptive management practices. This is particularly important in the CAO, where no robust regional management structures are in place. Recognizing how cross-scale dynamics can cause uncertainties to the current fisheries projections and implementing well-functioning adaptive management structures across different Arctic sub-systems can play a key role in whether the Blue Growth potential in Arctic fisheries is realized or lost.

Observed warming over northern South America has an anthropogenic origin

We investigate whether the recently observed trends in daily maximum and minimum near-surface air temperature (Tmax and Tmin, respectively) over South America (SA) are consistent with the simulated response of Tmin and Tmax to anthropogenic forcing. Results indicate that the recently observed warming in the dry seasons is well beyond the range of natural (internal) variability. In the wet season the natural modes of variability explain a substantial portion of Tmin and Tmax variability. We demonstrate that the large-scale component of greenhouse gas (GHG) forcing is detectable in dry-seasonal warming. However, none of the global and regional climate change projections reproduce the observed warming of up to 0.6 K/Decade in Tmax in 1983–2012 over northern SA during the austral spring (SON). Thus, besides the global manifestation of GHG forcing, other external drivers have an imprint. Using aerosols-only forcing simulations, our results provide evidence that anthropogenic aerosols also have a detectable influence in SON and that the indirect effect of aerosols on cloud’s lifetime is more compatible with the observed record. In addition, there is an increasing trend in the observed incoming solar radiation over northern SA in SON, which is larger than expected from natural (internal) variability alone. We further show that in the dry seasons the spread of projected trends based on the RCP4.5 scenario derived from 30 CMIP5 models encompasses the observed area-averaged trends in Tmin and Tmax. This may imply that the observed excessive warming in the dry seasons serve as an illustration of plausible future expected change in the region.

Functional Response Trait Analysis Improves Climate Sensitivity Estimation in Beech Forests at a Trailing Edge

Functional response traits influence the ability of species to colonize and thrive in a habitat and to persist under environmental challenges. Functional traits can be used to evaluate environment-related processes and phenomena. They also help to interpret distribution patterns, especially under limiting ecological conditions. In this study, we investigate landscape-scale functional distribution responses of beech forests in a climatic transitional zone in Europe. We construct empirical density distribution responses for beech forests by applying coping-resilience-failure climatic traits based on 27 bioclimatic variables, resulting in prevalence-decay-exclusion distribution response patterns. We also perform multivariate exploratory cluster analysis to reveal significant sets of response patterns from the resilience and adaptation aspects. Temperature-related distribution responses presented a prevalence-dominated functional pattern, with Annual mean temperature indicating the most favorable adaptation function. Precipitation indices showed climate-limited response patterns with the dominance of extinction function. Considering regional site-specific climate change projections, these continental beech forests could regress moderately due to temperature increase in the near future. Our results also suggest that both summer and winter precipitation could play a pivotal role in successful resilience. Functions and variables that indicate climate sensitivity can serve as a useful starting point to develop adaptation measures for regional forest management.

Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models

This study assessed the regional climate models (RCMs) employed in the Coordinated Regional climate Downscaling Experiment (CORDEX) South Asia framework to investigate the qualitative aspects of future change in seasonal mean near surface air temperature and precipitation over the Hindu Kush Himalayan (HKH) region. These RCMs downscaled a subset of atmosphere ocean coupled global climate models (AOGCMs) in the Coupled Model Intercomparison Project phase 5 (CMIP5) to higher 50 km spatial resolution over a large domain covering South Asia for two representation concentration pathways (RCP4.5 and RCP8.5) future scenarios. The analysis specifically examined and evaluated multi-model and multi-scenario climate change projections over the hilly sub-regions within HKH for the near-future (2036–2065) and far-future (2066–2095) periods. The downscaled multi-RCMs provide relatively better confidence than their driving AOGCMs in projecting the magnitude of seasonal warming for the hilly sub-region within the Karakoram and northwestern Himalaya, with higher projected change of 5.4 °C during winter than of 4.9 °C during summer monsoon season by the end of 21st century under the high-end emissions (RCP8.5) scenario. There is less agreement among these RCMs on the magnitude of the projected warming over the other sub-regions within HKH for both seasons, particularly associated with higher RCM uncertainty for the hilly sub-region within the central Himalaya. The downscaled multi-RCMs show good consensus and low RCM uncertainty in projecting that the summer monsoon precipitation will intensify by about 22% in the hilly sub-region within the southeastern Himalaya and Tibetan Plateau for the far-future period under the RCP8.5 scenario. There is low confidence in the projected changes in the summer monsoon and winter season precipitation over the central Himalaya and in the Karakoram and northwestern Himalaya due to poor consensus and moderate to high RCM uncertainty among the downscaled multi-RCMs. Finally, the RCM related uncertainty is found to be large for the projected changes in seasonal temperature and precipitation over the hilly sub-regions within HKH by the end of this century, suggesting that improving the regional processes and feedbacks in RCMs are essential for narrowing the uncertainty, and for providing more reliable regional climate change projections suitable for impact assessments in HKH region.

The Modulation of Stationary Waves, and Their Response to Climate Change, by Parameterized Orographic Drag

Abstract The parameterization of orographic drag processes in atmospheric models remains uncertain because of a lack of observational and theoretical constraints on their formulation and free parameters. While previous studies have demonstrated that parameterized orographic drag acting near the surface has a significant impact on the atmospheric circulation, this work follows a more systematic approach to investigate its impacts on the large-scale circulation and the circulation response to climate change. A set of experiments with a comprehensive atmospheric general circulation model is used to ascertain the range of climatological circulations that may arise from parameter uncertainty. It is found that the Northern Hemisphere (NH) wintertime stationary wave field is strongly damped over the North Pacific (NP) and amplified over the North Atlantic (NA) as a result of increased low-level parameterized orographic drag, both of which are shown to be conducive to higher-latitude westerlies. A comparison with the stationary wave field presented in other studies suggests that the too-zonal NA jet and equatorward NP jet biases that are prevalent in climate models may be at least partly due to their representation of orographic drag. The amplitude of the stationary wave response to climate change across the experiments is shown to scale with the magnitude of low-level parameterized orographic drag through its influence on the present-day climatological stationary wave amplitudes over different sectors of the NH, which is consistent with linear stationary wave theory. This work highlights the importance of fidelity in a model’s basic state for regional climate change projections.

Forest ecosystem services affected by natural disturbances, climate and land-use changes in the Tatra Mountains

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 73:57-71 (2017) - DOI: https://doi.org/10.3354/cr01461 Forest ecosystem services affected by natural disturbances, climate and land-use changes in the Tatra Mountains Peter Fleischer1,2, Viliam Pichler1, Peter Fleischer Jr.1, Ladislav Holko3, František Máliš1,4, Erika Gömöryová1,*, Pavel Cudlín5, Jan Holeksa6, Zuzana Michalová7, Zuzana Homolová2, Jaroslav Škvarenina1, Katarína Střelcová1, Pavol Hlaváč1 1Faculty of Forestry, Technical University in Zvolen, 96053 Zvolen, Slovakia 2Research Station of TANAP, State Forest of TANAP, 059 60 Tatranská Lomnica, Slovakia 3Institute of Hydrology, Slovak Academy of Sciences, 031 05 Liptovský Mikuláš, Slovakia 4Forest Research Institute Zvolen, National Forest Centre, 960 92 Zvolen, Slovakia 5Institute of Systems Biology and Ecology, Academy of Sciences, 370 05 České Budejovice, Czech Republic 6Faculty of Biology, Adam Mickiewicz University, 61 614 Poznań, Poland 7Faculty of Forestry and Wood Technology, Czech University of Life Sciences, 16 521 Prague, Czech Republic *Corresponding author: gomoryova@tuzvo.sk‑Advance View was available online June 28, 2017 ABSTRACT: The consequences of large-scale disturbances magnified by climate extremes and land-use changes in Norway spruce forests in the Tatra Mountains (Slovakia) are assessed in this study. The study area is part of the territory of Tatra National Park (TANAP). The driver-pressure-state-impact-response (DPSIR) framework was applied to evaluate how the ecosystem and its services are affected. The state of the ecosystem and its potential for provisioning ecosystem services before and after disturbances is expressed by a set of indicators derived mostly from long-term ecological research conducted in TANAP. The differences are classified by a standardised change index (CI). Ten years after the major windthrow disturbance in 2004, all ecosystem services were still below the pre-disturbance state. The most pronounced declines were found in cultural (average CI = 0.69) and provisioning (average CI = 0.86) ecosystem services. Regulating services are recovering faster (average CI = 0.97), with some indicators exceeding the state before the disturbances. Significant changes took place at the tree line, which is a new phenomenon not known from previous disturbances. Despite a gradual recovery of the ecosystem state and functioning, this analysis confirms that there is a serious risk of decline in forest ecosystem benefits according to regional climate change projections. It also indicates the increasing importance of sustainable forest management for safeguarding ecosystem services under changing conditions. KEY WORDS: Forest ecosystem state · Bark beetle outbreak · Long-term research Full text in pdf format PreviousNextCite this article as: Fleischer P, Pichler V, Fleischer P Jr, Holko L and others (2017) Forest ecosystem services affected by natural disturbances, climate and land-use changes in the Tatra Mountains. Clim Res 73:57-71. https://doi.org/10.3354/cr01461 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 73, No. 1-2. Online publication date: August 21, 2017 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2017 Inter-Research.

Assessing Stand-Level Climate Change Risk Using Forest Inventory Data and Species Distribution Models

Climate change is having important effects on forest ecosystems, presenting a challenge for natural resource professionals to reduce climate-associated impacts while still achieving diverse management objectives. Regional projections of climate change and forest response are becoming more readily available, but managers are still searching for practical ways to apply this information. We propose that commonly collected forest inventory data be used in conjunction with species distribution models to better understand the potential response of forests to climate change and inform management at the site level. In this article, we propose a new climate risk metric that incorporates stand-level forest inventory data with projections of tree species habitat from the Climate Change Tree Atlas. This climate risk metric can serve as a starting point for managers to consider how forests may be altered by climate change. We also describe two examples of how this metric was used in real-world management situations.

Statistical‐dynamical downscaling of precipitation for Vietnam: methodology and evaluation for the recent climate

ABSTRACTPrecipitation over Southeast Asia is primarily controlled by the Southeast Asian monsoon system. This area features complex orography and morphology, and has limited surface precipitation observations. In this study, a statistical‐dynamical downscaling approach that combines weather typing and dynamical downscaling is developed to obtain a high‐resolution precipitation climatology for tropical Southeast Asia. A transient simulation with the regional climate model COSMO‐CLM (COnsortium for Small Scale MOdelling‐Climate Limited‐area Modelling Community) driven by ERA‐Interim (1979–2008) is performed for the study region. Focussing on Vietnam, six weather types (WTs) are selected for the Indochina Peninsula during the wet season (April to October) using a k‐means cluster approach on daily 850 and 200 hPa zonal wind components from ERA‐Interim reanalysis. The six WTs can be physically interpreted as different stages of the seasonal progression of the planetary‐scale monsoon circulation. For each WT, selected representatives from the COSMO‐CLM run are dynamically downscaled to a resolution of 0.0625° × 0.0625° (∼7 km). Using the present‐day WT frequencies, the simulated COSMO‐CLM representatives at 7 km are recombined to a high‐resolution rainfall climatology for the recent decades. The resulting high‐resolution precipitation climatology is generally able to capture the present‐day precipitation estimates derived from APHRODITE (Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources) and station data. In spite of systematic biases our approach provides a valuable tool to obtain more robust regional climate change projections for the study area.

On the influence of simulated SST warming on rainfall projections in the Indo-Pacific domain: an AGCM study

Significant uncertainty exists in regional climate change projections, particularly for rainfall and other hydro-climate variables. In this study, we conduct a series of Atmospheric General Circulation Model (AGCM) experiments with different future sea surface temperature (SST) warming simulated by a range of coupled climate models. They allow us to assess the extent to which uncertainty from current coupled climate model rainfall projections can be attributed to their simulated SST warming. Nine CMIP5 model-simulated global SST warming anomalies have been super-imposed onto the current SSTs simulated by the Australian climate model ACCESS1.3. The ACCESS1.3 SST-forced experiments closely reproduce rainfall means and interannual variations as in its own fully coupled experiments. Although different global SST warming intensities explain well the inter-model difference in global mean precipitation changes, at regional scales the SST influence vary significantly. SST warming explains about 20–25% of the patterns of precipitation changes in each of the four/five models in its rainfall projections over the oceans in the Indo-Pacific domain, but there are also a couple of models in which different SST warming explains little of their precipitation pattern changes. The influence is weaker again for rainfall changes over land. Roughly similar levels of contribution can be attributed to different atmospheric responses to SST warming in these models. The weak SST influence in our study could be due to the experimental setup applied: superimposing different SST warming anomalies onto the same SSTs simulated for current climate by ACCESS1.3 rather than directly using model-simulated past and future SSTs. Similar modelling and analysis from other modelling groups with more carefully designed experiments are needed to tease out uncertainties caused by different SST warming patterns, different SST mean biases and different model physical/dynamical responses to the same underlying SST forcing.

Peer review report 1 on “Regional climate change projections of streamflow characteristics in the Northeast and Midwest U.S.”

Peer review report 1 on “Regional climate change projections of streamflow characteristics in the Northeast and Midwest U.S.”

Peer review report 2 on “Regional climate change projections of streamflow characteristics in the Northeast and Midwest U.S.”

Peer review report 2 on “Regional climate change projections of streamflow characteristics in the Northeast and Midwest U.S.”

Optimizing dynamic downscaling in one-way nesting using a regional ocean model

Dynamical downscaling with nested regional oceanographic models has been demonstrated to be an effective approach for both operationally forecasted sea weather on regional scales and projections of future climate change and its impact on the ocean. However, when nesting procedures are carried out in dynamic downscaling from a larger-scale model or set of observations to a smaller scale, errors are unavoidable due to the differences in grid sizes and updating intervals. The present work assesses the impact of errors produced by nesting procedures on the downscaled results from Ocean Regional Circulation Models (ORCMs). Errors are identified and evaluated based on their sources and characteristics by employing the Big-Brother Experiment (BBE). The BBE uses the same model to produce both nesting and nested simulations; so it addresses those error sources separately (i.e., without combining the contributions of errors from different sources). Here, we focus on discussing errors resulting from the spatial grids’ differences, the updating times and the domain sizes. After the BBE was separately run for diverse cases, a Taylor diagram was used to analyze the results and recommend an optimal combination of grid size, updating period and domain sizes. Finally, suggested setups for the downscaling were evaluated by examining the spatial correlations of variables and the relative magnitudes of variances between the nested model and the original data.

Coordinated Experiments for Projections of Regional Climate Change

We review coordinated efforts for producing regional climate projections through dynamical and statistical downscaling tools driven by global climate model output. Such projections are affected by multiple sources of uncertainty both at the global model and at the downscaling levels. The characterization of these uncertainties and the production of robust regional to local projections for use in impact studies require the completion of properly designed large ensembles of experiments. Toward this purpose, several regional coordinated efforts have been conducted in the past, particularly involving regional climate models, but because of the lack of a common experiment protocol, the transfer of know-how across them has been difficult. This problem is being addressed in the Coordinated Regional Downscaling Experiment (CORDEX), a framework designed to produce the next generation of worldwide high-resolution regional climate projections through a fully coordinated experiment protocol.

An evaluation of high‐resolution regional climate model simulations of snow cover and albedo over the Rocky Mountains, with implications for the simulated snow‐albedo feedback

AbstractThe snow‐albedo feedback (SAF) strongly influences climate over midlatitude mountainous regions. However, over these regions the skill of regional climate models (RCMs) at simulating properties such as snow cover and surface albedo is poorly characterized. These properties are evaluated in a pair of 7 year long high‐resolution RCM simulations with the Weather Research and Forecasting model over the central Rocky Mountains. Key differences between the simulations include the computational domain (regional versus continental) and land surface model used (Noah versus Noah‐MP). Simulations are evaluated against high‐resolution satellite estimates of snow cover and albedo from the Moderate Resolution Imaging Spectroradiometer. Both simulations generally reproduce the observed seasonal and spatial variability of snow cover and also exhibit important biases. One simulation substantially overpredicts subpixel fractional snow cover over snowy pixels (by up to 0.4) causing large positive biases in surface albedo, likely due in part to inadequate representation of canopy effects. The other simulation exhibits a negative bias in areal snow extent (as much as 19% of the analysis domain). Surface measurements reveal large positive biases in snow albedo (exceeding 0.2) during late spring caused by neglecting radiative effects of impurities deposited onto snow. Semi‐idealized climate change experiments show substantially different magnitudes of SAF‐enhanced warming in the two simulations that can be tied to the differences in snow cover in their control climates. More confident projections of regional climate change over mountains will require further work to evaluate and improve representation of snow cover and albedo in RCMs.

The impact of SST biases on projections of anthropogenic climate change: A greater role for atmosphere‐only models?

AbstractThere is large uncertainty in the model simulation of regional climate change from anthropogenic forcing. Recent studies have tried to link such uncertainty to intermodel differences in the pattern of sea surface temperature (SST) change. On the other hand, coupled climate models also contain systematic biases in their climatology, largely due to drift in SSTs. To the extent that the projected changes depend on the mean state, biases in the present‐day climatology also contribute to the intermodel spread in climate change projections. By comparing atmospheric general circulation model (AGCM) simulations using the climatological SSTs from different coupled models, we show that biases in the climatological SST generally have a larger impact on regional projections over land than do intermodel differences in the pattern of SST change. These results advocate for a greater application of AGCM simulations with observed SSTs or flux‐adjusted coupled models to improve regional projections of anthropogenic climate change.