Climate change at the landscape scale: predicting fine‐grained spatial heterogeneity in warming and potential refugia for vegetation (original) (raw)
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Climate downscaling: techniques and application
Climate Research, 1996
Downscaling, or translation across scales, is a term adopted in recent years to describe a set of techniques that relate local-and regional-scale climate variables to the larger scale atmospheric forcing. Conceptually, this is a direct evolution of more traditional techniques in synoptic climatology; however, the downscaling approach was developed specifically to address present needs in global environmental change research, and the need for more detailed temporal and spatial information from Global Climate Models (GCMs). Two general categories exist for downscaling techniques: process based techniques focused on nested models, and empirical techniques using one form or another of transfer function between scales. While in the long term nested models hold the greatest promise for regional-scale analysis, this approach is still in development, requires detailed surface climate data, and is dependent on high end computer availability. Conversely, empirical relationships offer a more immediate solution and significantly lower computing requirements, consequently offering an approach that can be rapidly adopted by a wider community of scientists. In this paper, an application of empirical downscaling of regional precipitation is implemented to demonstrate its effectiveness for evaluating GCM simulations and developing regional climate change scenarios. Gridded analyses of synoptic-scale circulation fields are related to regional precipitation using neural nets. Comparable GCM circulation fields are then used with the derived relat~onships to investigate control simulation and doubled atmospheric CO2 simulation synoptic-scale forcing on regional climates.
Fine-scale climate change: modelling spatial variation in biologically meaningful rates of warming
Global Change Biology, 2016
The existence of fine-grain climate heterogeneity has prompted suggestions that species may be able to survive future climate change in pockets of suitable microclimate, termed 'microrefugia'. However, evidence for microrefugia is hindered by lack of understanding of how rates of warming vary across a landscape. Here we present a model that is applied to provide fine-grained, multi-decadal estimates of temperature change based on the underlying physical processes that influence microclimate. Weather station and remotely-derived environmental data were used to construct physical variables that capture the effects of terrain, sea-surface temperatures, altitude and surface albedo on local temperatures, which were then calibrated statistically to derive gridded estimates of temperature. We apply the model to the Lizard Peninsula, United Kingdom to provide accurate (mean error = 1.21ºC; RMS error = 1.63ºC) hourly estimates of temperature at a resolution of 100 m for the period 1977 to 2014. We show that rates of warming vary across a landscape primarily due to longterm trends in weather conditions. Total warming varied from 0.87 to 1.16ºC, with the slowest rates of warming evident on northeast facing slopes. This variation contributed to substantial spatial heterogeneity in trends in bioclimatic variables: for example, the change in the length of the frost-free season varied from +11 to-54 days and the increase annual growing degree-days from 51 to 267 ºC days. Spatial variation in warming was caused primarily by a decrease in daytime cloud cover with a resulting increase in received solar radiation, and secondarily by a decrease in the strength of westerly winds, which has amplified the effects on temperature of solar radiation on west-facing slopes. We emphasise the importance of multi-decadal trends in weather conditions in determining spatial variation in rates of warming, suggesting that locations experiencing least warming may not remain consistent under future climate change.
2011
Global Climate Models (GCMs) are the typical sources of future climate data required for impact assessments of climate change. However, GCM outputs are related to model-related uncertainties and involve a great deal of biases. Bias correction of model outputs is, therefore, necessary before their use in impact studies. The coarse resolution of GCM simulations is another hindrance to their direct use in fine-scale impact analysis of climate change. Although downscaling of GCM outputs can be performed by dynamical downscaling using Regional Climate Models (RCMs), it requires large computational capacity. When daily climate data from multiple GCMs are required to be downscaled, dynamical downscaling may not be a feasible option. Statistical downscaling, in contrast, can be efficiently used to downscale a large number of GCM outputs at a fine temporal and spatial scale. This study performs the bias correction and statistical downscaling of daily maximum and minimum temperature and daily precipitation data from six GCM and four RCM simulations for the northeast United Stated (US). The spatial resolution of the data set is 1 / 8°x 1 / 8° and it spans from 2046 to 2065. This finescale daily climate data set, which has been created using Bias Correction and Spatial Downscaling (BCSD) approach, can be directly used in regional impact studies for the northeast US. v Contents Chapter 1 Introduction……………………………………………………………………………………………………………………………………………………………………………………………1 1.1 Background and Motivation……………………………………………………………………………………………………………………………………………………………1 1.2 Literature Review……………………………………………………………………………………………………………………………………………………………………………………………6 1.3 Research Objectives………………………………………………………………………………………………………………………………………………………………………………. ..12 1.4 Organization of the Thesis……………………………………………………………………………………………………………………………………………………………. .13 Chapter 2 Data and Methods…………………………………………………………………………………………………………………………………………………………………. 14 2.1 Description of Data…………………………………………………………………………………………………………………………………………………………………………………. ..14 2.2 Methodology of Bias Correction and Spatial Downscaling………………………………………………………. .16 2.3 Climate Extreme………………………………. …………………………………………………………………………………………………………………………………………………………...19 Chapter 3 Results and Discussions……………………………………………………………………………………………………………………………………………. .21 3.1 Bias Correction and Spatial Downscaling of Daily Temperature Data……………………. .21 3.2 Importance of Bias Correction………………………………………………………………………………………………………………………………………………. ..24 3.3 Extreme Climate Analysis……………………………………………………………………………………………………………………………………………………………. .30 3.3.1 Temperature Extremes…………………………………………………………………………………………………………………………………………. .30 3.3.2 Precipitation Extremes…………………………………………………………………………………………………………………………………………. .35 Chapter 4 Conclusion………………………………………………………………………………………………………………………………………………………………………………………………. 40
Climate downscaling techniques and applications
Climate Research, 1996
Downscaling, or translation across scales, is a term adopted in recent years to describe a set of techniques that relate local-and regional-scale climate variables to the larger scale atmospheric forcing. Conceptually, this is a direct evolution of more traditional techniques in synoptic climatology; however, the downscaling approach was developed specifically to address present needs in global environmental change research, and the need for more detailed temporal and spatial information from Global Climate Models (GCMs). Two general categories exist for downscaling techniques: process based techniques focused on nested models, and empirical techniques using one form or another of transfer function between scales. While in the long term nested models hold the greatest promise for regional-scale analysis, this approach is still in development, requires detailed surface climate data, and is dependent on high end computer availability. Conversely, empirical relationships offer a more immediate solution and significantly lower computing requirements, consequently offering an approach that can be rapidly adopted by a wider community of scientists. In this paper, an application of empirical downscaling of regional precipitation is implemented to demonstrate its effectiveness for evaluating GCM simulations and developing regional climate change scenarios. Gridded analyses of synoptic-scale circulation fields are related to regional precipitation using neural nets. Comparable GCM circulation fields are then used with the derived relat~onships to investigate control simulation and doubled atmospheric CO2 simulation synoptic-scale forcing on regional climates.
A cautionary note on automated statistical downscaling methods for climate change
Climatic Change, 2013
The urge for higher resolution climate change scenarios has been widely recognized, particularly for conducting impact assessment studies. Statistical downscaling methods have shown to be very convenient for this task, mainly because of their lower computational requirements in comparison with nested limited-area regional models or very high resolution Atmosphere-ocean General Circulation Models. Nevertheless, although some of the limitations of statistical downscaling methods are widely known and have been discussed in the literature, in this paper it is argued that the current approach for statistical downscaling does not guard against misspecified statistical models and that the occurrence of spurious results is likely if the assumptions of the underlying probabilistic model are not satisfied. In this case, the physics included in climate change scenarios obtained by general circulation models, could be replaced by spatial patterns and magnitudes produced by statistically inadequate models. Illustrative examples are provided for monthly temperature for a region encompassing Mexico and part of the United States. It is found that the assumptions of the probabilistic models do not hold for about 70 % of the gridpoints, parameter instability and temporal dependence being the most common problems. As our examples reveal, automated statistical downscaling "black-box" models are to be considered as highly prone to produce misleading results. It is shown that the Probabilistic Reduction approach can be incorporated as a complete and internally consistent framework for securing Climatic Change the statistical adequacy of the downscaling models and for guiding the respecification process, in a way that prevents the lack of empirical validity that affects current methods.
Climate Dynamics, 2004
The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller sub-area. This setup permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment has been carried out for four winter months over the west coast of North America. The results show that complex topography and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings, found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility. Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution.
The effect of spatial resolution on projected responses to climate warming
Diversity and Distributions, 2012
To determine how changing the resolution of modelled climate surfaces can affect estimates of the amount of thermally suitable habitat available to species under different levels of warming. (B) Location Lake Vyrnwy RSPB Reserve, which covers around 9,700 hectares of a topographically diverse landscape in Wales. (B) Methods A recently published microclimate model was used to predict maximum, minimum and mean temperatures at 5 x 5 m resolution for the study site, under current and possible future conditions. These temperature surfaces were then averaged to produce coarser resolution surfaces, up to a maximum of 1 x 1 km resolution. Ground beetles were collected using pitfall traps between May and August 2008. GLMs were fitted to the temperature surfaces to predict the amount of landscape suitable for a northerly-distributed ground beetle, Carabus glabratus, and the most southerly distributed ground beetle found at the site, Poecilus versicolor, under current and possible future conditions. (B) Results A wider range of temperatures are expected within our site when temperature is modelled at finer resolutions. Fitting GLMs at different resolutions resulted in the inclusion of different
A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain
Journal of Climate, 2012
Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50-200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November-May precipitation totals (.300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.
A Review of Downscaling Methods for Climate Change Projections
Adjustment of modeled values to reflect the observed distribution and statistics. Change factor (CF): Ratio between values of current climate and future GCM simulations. Climatology: Long-term average of a given variable, often over time periods of 20 to 30 years. For example, a monthly climatology consists of a mean value for each month computed over 30 years, and a daily climatology consists of a mean value for each day. Coastal breeze: Wind in coastal areas driven by differences in the rate of cooling/warming of land and water. Convective precipitation: Intense precipitation of short duration that characterizes most of the rainfall in the tropics. Direct and indirect effect of aerosols: Atmospheric aerosols are solid and liquid particles suspended in air that influence the amount of solar radiation that reaches the surface of the Earth. Aerosols can cool the surface of the Earth via reflection of solar radiation. This is termed the direct effect. The effect of aerosols on the radiative properties of Earth's cloud cover is referred to as the indirect effect. Downscaling: Derivation of local to regional-scale (10-100 kilometers) information from larger scale modeled or observed data. There are two main approaches: dynamical downscaling and statistical downscaling. Emissions Scenario: Estimates of future greenhouse gas emissions released into the atmosphere. Such estimates are based on possible projections of economic and population growth and technological development, as well as physical processes within the climate system. Feedback (climate): An interaction within the climate system in which the result of an initial process triggers changes in a second process that in turn influences the initial one. A positive feedback intensifies the original process and a negative one reduces it. Frequency distribution: An arrangement of statistical data that shows the frequency of the occurrence of different values. General Circulation Model (GCM): A global, three-dimensional computer model of the climate system that can be used to simulate human-induced climate change. GCMs represent the effects of such factors as reflective and absorptive properties of atmospheric water vapor, greenhouse gas concentrations, clouds, annual and daily solar heating, ocean temperatures, and ice boundaries. Grid cell: A rectangular area that represents a portion of the Earth's surface. Interannual variability: Year-to year change in the mean state of the climate that is caused by a variety of factors and interactions within the climate system. One important example of interannual variability is the quasi-periodic change of atmospheric and oceanic circulation patterns in the Tropical Pacific region, collectively known as El Niño-Southern Oscillation (ENSO). A Review of Downscaling Methods for Climate Change Projections vi Interpolation: The process of estimating unknown data values that lie between known values. Various interpolation techniques exist. One of the simplest is linear interpolation, which assumes a constant rate of change between two points. Unknown values anywhere between these two points can then be assigned. Land-sea contrast: Difference in pressure and other atmospheric characteristics that arises between the land and ocean, caused by the difference in the rate of cooling/warming of their respective surfaces. Large-scale climate information: Atmospheric characteristics (e.g., temperature, precipitation, relative humidity) spanning several hundred to several thousand kilometers. Lateral boundaries: Information about the air masses, obtained from GCM output or observations, used by RCMs to derive fine-scale information. Markovian process: When values of the future depend solely on the present state of the system and not the past. Predictand: The variable that is to be predicted. In downscaling, the predictand is the local climate variable of interest. Predictor: A variable that can be used to predict the value of another variable. In downscaling, the predictor is the large-scale climate variable. Regional Climate Model (RCM): High-resolution (typically 50 kilometers) computer model that represents local features. It is constructed for limited areas, run for periods of ~20 years, and driven by large-scale data. Spatial downscaling: Refers to the methods used to derive climate information at finer spatial resolution from coarser spatial resolution GCM output. The fundamental basis of spatial downscaling is the assumption that significant relationships exist between local and large-scale climate. Spatial resolution: In climate, spatial resolution refers to the size of a grid cell in which 10-80 kilometers and 200-500 kilometers are considered to be "fine" and "coarse," respectively. Stationarity: Primary assumption of statistical downscaling; as the climate changes, the statistical relationships do not. It assumes that the statistical distribution associated with each climate variable will not change, that the same large-scale predictors will be identified as important, and that the same statistical relationships between predictors and predictands exist. Stochastic: Describes a process or simulation in which there is some indeterminacy. Even if the starting point is known, there are several directions in which the process can evolve, each with a distinct probability. Synoptic: Refers to large-scale atmospheric characteristics spanning several hundred to several thousand kilometers. Systematic bias: The difference between the observed data and modeled results that occurs due model imperfections. Temporal downscaling: Refers to the derivation of fine scale temporal data from coarser-scale temporal information (e.g., daily data from monthly or seasonal information). Its main application is in impact studies when impact models require daily or even more frequent information. Temporal resolution: The time scale at which a measurement is taken or a value is represented. Daily and monthly resolutions denote one value per day and one value per month, respectively.