ALBERT

Description: Climate change will bring about significant changes to the capacity of, and the demand on, water resources. The resulting changes include increasing climate variability that is expected to affect hydrologic conditions. The effects of climate variability on various meteorological variables have been extensively observed in many regions around the world. Atmospheric circulation, topography, land use and other regional features modify global changes to produce unique patterns of change at the regional scale. As the future changes to these water resources cannot be measured in the present, hydrological models are critical in the planning required to adapt our water resource management strategies to future climate conditions. Such models include catchment runoff models, reservoir management models, flood prediction models, groundwater recharge and flow models, and crop water balance models. In water-scarce regions such as Australia, urban water systems are particularly vulnerable to rapid population growth and climate change. In the presence of climate change induced uncertainty, urban water systems need to be more resilient and multi-sourced. Decreasing volumetric rainfall trends have an effect on reservoir yield and operation practices. Severe intensity rainfall events can cause failure of drainage system capacity and subsequent urban flood inundation problems. Policy makers, end users and leading researchers need to work together to develop a consistent approach to interpreting the effects of climate variability and change on water resources. This Special Edition includes papers by international experts who have investigated climate change impacts on a variety of systems including irrigation and water markets, land use changes and vegetation growth, lake water levels and quality and sea level rises. These investigations have been conducted in many regions of the world including the USA, China, East Africa, Australia, Taiwan and the Sultanate of Oman.

Description: The Antarctic Circumpolar Current (ACC), with its associated three-dimensional circulation, plays an important role in global climate. This study concentrates on surface signatures of recent climate change in the ACC region and on mechanisms that control this change. Examination of climate model simulations shows that they match the observed late 20th century sea-surface temperature (SST) trends averaged over this region quite well, despite underestimating the observed surface-wind increases. Such wind increases, however, are expected to lead to significant cooling of the region, contradicting the observed SST trends. Motivated by recent theories of the ACC response to variable wind and radiative forcing, the authors used two idealized models to assess contributions of various dynamical processes to the SST evolution in the region. In particular, a high-resolution channel model of the ACC responds to increasing winds by net surface ACC warming due to enhanced mesoscale turbulence and associated heat transports in the mixed layer. These fluxes, modeled, in a highly idealized fashion, via increased lateral surface mixing in a coarse-resolution hybrid climate model, substantially offset zonally non-uniform surface cooling due to air-sea flux and Ekman-transport anomalies. These results suggest that the combination of these opposing effects must be accounted for when estimating climate response to any external forcing in the ACC region.

Description: The bulk of our knowledge about causes of 20 th century climate change comes from simulations using numerical models. In particular, these models seemingly reproduce the observed nonuniform global warming, with periods of faster warming in 1910–1940 and 1970–2000, and a pause in between. However, closer inspection reveals some differences between the observations and model simulations. Here we show that observed multidecadal variations of surface climate exhibited a coherent global-scale signal characterized by a pair of patterns, one of which evolved in sync with multidecadal swings of the global temperature, and the other in quadrature with them. In contrast, model simulations are dominated by the stationary — single pattern — forced signal somewhat reminiscent of the observed “in-sync” pattern most pronounced in the Pacific. While simulating well the amplitude of the largest-scale — Pacific and hemispheric — multidecadal variability in surface temperature, the model underestimates variability in the North Atlantic and atmospheric indices.

Description: This study disentangles causes of the Michigan-Huron system lake-level variability. Regional precipitation is identified as the primary driver of lake levels with sub-monthly time lag, implying that the lake-level time series can be used as a proxy for regional precipitation throughout most of the 1865–present instrumental record. Aside from secular variations associated with the Atlantic Multidecadal Oscillation, the lake-level time series is dominated by two near-decadal cycles with periods of 8 and 12 years. A combination of correlation analysis and compositing suggests that the 8-y cycle stems from changes in daily wintertime precipitation amounts associated with individual storms, possibly due to large-scale atmospheric flow anomalies that affect moisture availability. In contrast, the 12-y cycle is caused by changes in the number of instances, or frequency, of summertime convective precipitation due to a preferred upper-air trough pattern situated over the Great Lakes. In recent decades, the lake-level budget exhibited an abnormal—relative to the remainder of the instrumental record—evaporation-driven trend, likely connected to regional signatures of anthropogenic climate change. The latter effect must be accounted for, along with the effects of precipitation, when assessing possible scenarios of future lake-level variability.

Description: Identification and dynamical attribution of multidecadal climate undulations to either variations in external forcings or to internal sources is one of the most important topics of modern climate science, especially in conjunction with the issue of human induced global warming. Here we utilize ensembles of 20 th century climate simulations to isolate the forced signal and residual internal variability in a network of observed and modeled climate indices. The observed internal variability so estimated exhibits a pronounced multidecadal mode with a distinctive spatiotemporal signature, which is altogether absent in model simulations. This single mode explains a major fraction of model–data differences over the entire climate-index network considered; it may reflect either biases in the models’ forced response or models’ lack of requisite internal dynamics, or a combination of both.

Description: ABSTRACT This paper combines CMIP5 historical simulations and observations of surface temperature to investigate relative contributions of forced and internal climate variability to long-term climate trends. A suite of estimated forced signals based on surrogate multi-model ensembles mimicking the statistical characteristics of individual models is used to show that, in contrast to earlier claims, scaled versions of the multi-model ensemble mean cannot adequately characterize the full spectrum of CMIP5 forced responses, due to misrepresenting the model uncertainty. The same suite of multiple forced signals is also used to derive unbiased estimates of the model simulated internal variability in historical simulations and, after appropriate scaling to match the observed climate sensitivity, to estimate the internal component of climate variability in the observed temperatures. On average, climate models simulate the non-uniform warming of Northern Hemisphere mean surface temperature well, but are overly sensitive to forcing in the North Atlantic and North Pacific, where the simulations have to be scaled back to match observed trends. In contrast, the simulated internal variability is much weaker than observed. There is no evidence of coupling between the model simulated forced signals and internal variability, suggesting that their underlying dominant physical mechanisms are different. Analysis of regional contributions to the recent global warming hiatus points to the presence of a hemispheric mode of internal climate variability, rather than to internal processes local to the Pacific Ocean. Large discrepancies between present estimates of the simulated and observed multidecadal internal climate variability suggest that our ability to attribute and predict climate change using current generation of climate models is limited. This paper combines CMIP5 historical simulations and observations of surface temperature to investigate relative contributions of forced and internal climate variability to long-term climate temperature trends. A Monte-Carlo methodology is developed to derive unbiased estimates of the model simulated internal variability in historical simulations and, after appropriate scaling to match the observed climate sensitivity, to estimate the internal component of climate variability in the observed temperatures. It is shown that the simulated internal variability is much weaker than observed. Standard deviation (STD) of the observed and simulated internal variability in the Atlantic Multidecadal Oscillation (AMO) index derived from historical CMIP5 runs and the observed 20th-century temperatures, as well as the STDs based on the CMIP5 pre-industrial control runs. The STDs shown are computed from raw and running-mean boxcar low-pass filtered data, with the filter window size indicated in the abscissa of the plot. Error bars show the uncertainty of the STD estimates. The estimated observed internal variability substantially exceeds that in the CMIP5 simulations.

Description: Air temperature variability on different time scales exhibits recurring patterns and quasi-oscillatory phenomena. Climate oscillations with the period about 7–8 years have been observed in many instrumental records in Europe. Although these oscillations are weak if considering their amplitude, they might have non-negligible influence on temperature variability on shorter time scales due to cross-scale interactions recently observed by Paluš [Phys. Rev. Lett. 112, 078702, (2014)]. In order to quantify the cross-scale influence we propose a simple conditional mean approach which estimates the effect of the cycle with the period close to eight years on the amplitude of the annual cycle in surface air temperature (SAT) in the range 0.7–1.4  ∘ C and the effect on the overall variability of the SAT anomalies (SATA) leads to the changes 1.5–1.7  ∘ C in the annual SATA means. The strongest effect in the winter SATA means reaches 4–5  ∘ C in central European station and reanalysis data.

Description: The scope of this work is stochastic emulation of sea level pressure (SLP) for use in error estimation and statistical prediction studies. The input SLP dataset whose statistics are to be emulated was taken from the 1979–2013 ERA-Interim dataset at full 6-hourly temporal and 0.75° spatial resolutions over the Northern Hemisphere. Upon subtracting the monthly climatological mean value and mean diurnal cycle, the SLP anomalies (SLPA) were projected onto the subspace of 1000 leading empirical orthogonal functions of the daily-mean SLPA, which account for the vast majority (〉99%) of the full 6-hourly fields’ variance for each season. The main step of this method is the estimation of a linear autoregressive moving-average empirical model for the daily SLPA principal components (PCs) via regularized multiple linear regression; this model was driven, at the stage of simulation, by state-dependent (multiplicative) noise. Last, a diagnostic statistical scheme has been developed and implemented for accurate interpolation of simulated daily SLPA to 6-hourly temporal resolution. Upon transforming the simulated 6-hourly SLPA PCs into the physical space and adding a seasonal climatological mean and mean diurnal cycle, the resulting SLP variability was compared with the actual variability in the ERA-Interim dataset. It is shown that this empirical model produces independent realizations of SLP variability that are nearly indistinguishable from the observed variability over a wide range of statistical measures; these measures include, among others, spatial patterns of bandpass- and low-pass-filtered variability, as well as diverse characteristics of midlatitude cyclone tracks.