Generating weather for climate impact assessment on lakes

Lakes are driven in part by weather and they are affected by climate. The complex physical and biological processes that govern their behaviour makes it impossible to estimate their reactions to changed climatic conditions in a trivial manner. This work presents two weather generators (WGs), which are stochastic abstractions of observed meteorological time series, as tools for climate impact assessment on lakes. They enable to define "what if"-scenarios based on prescribed temperature changes, that are propagated to the rest of the generated variables, thus producing statistically balanced time series. For propagating the changes, linear and non-linear models of dependence were explored. The linear model consists of a Vector-Autoregressive process and the non-linear of a pair-wise copula construction method. Both methods are enhanced by phase randomization, a technique that uses the Fourier transform to generate "surrogate data" and helps to maintain longer-term dependencies in this context. The thesis also proposes a novel way to generate precipitation which works by estimating dryness probability from the state of non-precipitation variables and transforming the result to construct a time series without dry gaps. An upside to this treatment of precipitation is that it does not require a precipitation occurrence model and no different parameterizations for wet and dry states for the non-precipitation variables. The WGs were tested for their ability to extrapolate from colder towards warmer weather. While the more complex, copula-based method performed better than the simpler, linear method, it is shown that only relying on statistical relationships can mis-project the changes in dependent variables that accompany temperature increases. The two WGs are contrasted with a non-parametric K-Nearest Neighbors resampler, highlighting differences between those approaches and underlining specific weaknesses. The parametric WGs overestimate spread, while the resampler lacks variability resulting from a tendency to choose central values in both a uni- and multivariate sense

A weather generator based on vine copulas and phase randomization for producing scenarios

&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;Using weather generators to produce scenarios with changed statistics commonly involves the output of numerical climate models and/or leveraging the correlations in an observed data set. This contribution proposes vine copulas as a method to improve the latter in a parsimonious way. The vine copula construction flexibly models multivariate dependence structures as it breaks these down into pair-wise relationships that can be modelled individually with the wide variety of bivariate copula families. Setting up the vine tree carefully allows a user-supplied change in one specific variable, e.g. air temperature, to spread to the other simulated variables according to the fitted dependence structure.&amp;lt;br&amp;gt;In order not to increase dramatically the number of free parameters, the copula is only employed for time-invariant, inter-variate dependence, leaving all temporal and inter-site dependencies to Phase Randomization. Phase Randomization is a spectral method which generates &amp;quot;surrogate time series&amp;quot; that share their autocorrelation function with a source time series. It can be modified to handle cross-correlations in multivariate time series as well.&amp;lt;br&amp;gt;Precipitation occurrence and amounts are simulated in a joint fashion, using a latent variable constructed with information from other meteorological variable at the same locations. &amp;amp;#160;The methodology will be illustrated with an example involving daily air temperature, precipitation, sunshine duration and relative humidity from measurement stations in southern Germany.&amp;amp;#160;&amp;lt;/p&amp;gt; </jats:p

Ensemble Modeling of the Impact of Climate Warming and Increased Frequency of Extreme Climatic Events on the Thermal Characteristics of a Sub-Tropical Lake

Lake ecosystems are impacted by changes in climatic conditions. Climate changes forecasted to occur are reflected in models by slow gradual changes over extended periods of time. Output from weather generators, on the other hand, can simulate short-term extreme conditions and weather patterns. In order to evaluate the likely impact of climate changes on a large sub-tropical lake, specifically the thermal regime of the lake, we constructed climate scenarios using a weather generator. The 30-year scenarios included no change in climate conditions, a gradual change, increased frequency of heat waves and a merging of the latter two. The projected impact on the lake&rsquo;s physical properties was evaluated using an ensemble of 1-D hydrodynamic lake models. The gradual increase scenario had the largest impact on annual temperatures and stratification period; however, increased heat waves had a large effect on the summer lake conditions and introduced a larger degree of variability in water temperature. The use of the ensemble of models resulted in variability in the projected impacts; yet, the large degree of similarity between projected trends and patterns increased confidence in the results. The projected effect the heat waves will have on the lake conditions highlights the need to include heat waves in climate studies and the need for impact studies in order to better understand possible consequences for lake ecosystems

Ensemble Modeling of the Impact of Climate Warming and Increased Frequency of Extreme Climatic Events on the Thermal Characteristics of a Sub-Tropical Lake

Lake ecosystems are impacted by changes in climatic conditions. Climate changes forecasted to occur are reflected in models by slow gradual changes over extended periods of time. Output from weather generators, on the other hand, can simulate short-term extreme conditions and weather patterns. In order to evaluate the likely impact of climate changes on a large sub-tropical lake, specifically the thermal regime of the lake, we constructed climate scenarios using a weather generator. The 30-year scenarios included no change in climate conditions, a gradual change, increased frequency of heat waves and a merging of the latter two. The projected impact on the lake’s physical properties was evaluated using an ensemble of 1-D hydrodynamic lake models. The gradual increase scenario had the largest impact on annual temperatures and stratification period; however, increased heat waves had a large effect on the summer lake conditions and introduced a larger degree of variability in water temperature. The use of the ensemble of models resulted in variability in the projected impacts; yet, the large degree of similarity between projected trends and patterns increased confidence in the results. The projected effect the heat waves will have on the lake conditions highlights the need to include heat waves in climate studies and the need for impact studies in order to better understand possible consequences for lake ecosystems.</jats:p