Emerging Multi-year Predictability of Heatwave-Driven Cooling Demand

Estimating the characteristics of the Urban Heat Island Effect in Nicosia, Cyprus, using multiyear urban and rural climatic data and analysis

Electrical energy usage of commercial buildings (over 60% in the USA) is of growing concern due to its impact on the environment. To reduce it, it is necessary to identify the factors contributing to commercial building energy usage. Towards this, we studied the importance of various building features and their contributions on cooling energy, using two datasets of Commercial Building Energy Consumption Survey (CBECS) 2018 and 2012. For this, Shapash, an explainable artificial intelligence technique, was used, with Random Forest, to predict the cooling energy usage intensity (EUI). The two most important features for 2018 are cooling degree days and principal building activity, while cooling degree days and cooling percentage were for 2012 with 54.79% and 33.46% contributions of the total consumption respectively. The comparison of feature importance indicated that cooling degree days are the topmost important feature impacting cooling EUI and have a more significant contribution of 34.29% for 2018 and 19.68% for 2012. Overall, the most important features impacting cooling EUI were cooling degree days, principle building activity, cooling percentage and total hours open per week. The results from this study provide insights into the most significant factors influencing energy consumption and help in developing strategies for reducing it.

In cases where a quick insight into the operation of an HVAC system is more important than accuracy, cooling degree days can be used for monitoring electric energy consumption dependent on meteorological conditions. Cooling degree days are calculated from differences between outdoor temperatures above the base temperature and the base temperature itself, therefore containing both climate and building information. The difficulties in applying this method are the determination of base temperature and choosing a procedure for calculating degree days, which vary depending on the resolution of the weather data used. In addition, the cooling degree method has a major flaw, i.e. it considers only a linear dependence between cooling energy consumption and sensible cooling load, thereby ignoring latent loads, which become more significant at higher outdoor temperatures. In this article an analysis of real electric energy consumption data using the cooling degree method and an improved method derived from it that includes latent loads, as well as a comparison between them, are shown. Both methods are applied several times to metered data, each time with a different combination of a method for determining base temperature and a degree day calculation technique.

Vulnerability of ski tourism towards internal climate variability and climate change in the Swiss Alps

<p>Vegetation in the northern high latitudes shows a characteristic pattern of persistent changes as documented by multi-decadal satellite observations. The prevailing explanation that these mainly increasing trends (greening) are a consequence of external CO<sub>2</sub> forcing, i.e., due to the ubiquitous effect of CO2-induced fertilization and/or warming of temperature-limited ecosystems, however does not explain why some areas also show decreasing trends of vegetation cover (browning). We propose here to consider the dominant mode of multi-decadal internal climate variability in the north Atlantic region, the Atlantic Multidecadal Variability (AMV), as the missing link in the explanation of greening and browning trend patterns in the northern high latitudes. Such a link would also imply potential for decadal predictions of ecosystem changes in the northern high latitudes.</p><p>An analysis of observational and reanalysis data sets for the period 1979-2019 shows that locations characterized by greening trends largely coincide with warming summer temperature and increasing precipitation. Wherever either cooling or decreasing precipitation occurs, browning trends are observed over this period. These precipitation and temperature patterns are significantly correlated with a North Atlantic sea surface temperature index that represents the AMV signal, indicating its role in modulating greening/browning trend patterns in the northern high latitudes.</p><p>Using two large ensembles of coupled Earth system model simulations (100 members of MPI-ESM-LR Grand Ensemble and 32 members of the IPSL-CM6A-LR Large Ensemble), we separate the greening/browning pattern caused by external CO<sub>2</sub> forcing from that caused by internal climate variability associated with the AMV. These sets of model simulations enable a clean separation of the externally forced signal from internal variability. While the greening and browning patterns in the simulations do not agree with observations in terms of magnitude and location, we find consistent internally generated greening/browning patterns in both models caused by changes in temperature and precipitation linked to the AMV signal. These greening/browning trend patterns are of the same magnitude as those caused by the external forcing alone. Our work therefore shows that internally-generated changes of vegetation in the northern lands, driven by AMV, are potentially as large as those caused by external CO<sub>2</sub> forcing. We thus argue that the observed pattern of greening/browning in the northern high latitudes could originate from the combined effect of rising CO<sub>2</sub> as well as the AMV.</p>

We examine the internal climate variability of a 1000 year long integration of the third version of the Hadley Centre coupled model (HadCM3). The model requires no flux adjustment, needs no spin up procedure prior to coupling and has a stable climate in the global mean. The principal aims are (1) to validate the internal climate variability against observed climate variability, (2) to examine the model for any periodic modes of variability, (3) to use the model estimate of internal climate variability to asses the probability of occurrence of observed trends in climate variables, and (4) to compare HadCM3 with the previous version of the Hadley Centre model, HadCM2. The magnitude and frequency characteristics of the variability of the global mean surface temperature of HadCM3 on annual to decadal time scales is in good agreement with the observations. Observed upward trends in temperature over the last 20 years and longer are inconsistent with the internal variability of the model. The simulated spatial pattern of surface temperature variability is qualitatively similar to that observed, although there is an overestimation of the land temperature variability and regional errors in ocean temperature variability. The model simulates an El Nino Southern Oscillation with an irregular 3–4 year cycle, and with a teleconnection pattern which is much more like the observations than was found in HadCM2. The interdecadal variability of the model ocean in the tropical Pacific, North Pacific and North Atlantic is broadly similar to that in the real world with none of the simulated patterns having any periodic behaviour. HadCM3 simulates an Arctic Oscillation/North Atlantic Oscillation (NAO) in Northern Hemisphere winter which has a spatial pattern consistent with the observations in the Atlantic region, but has too much teleconnection with the North Pacific. The recent observed upward trend in the NAO index is inconsistent with the model internal variability. The variability of the simulated zonal mean atmospheric temperature shows some marked differences to the observed zonal mean temperature variability, although the comparison is confounded by the sparse observational network and its possible contamination by a climate change signal.

The origin of characteristic increase of continental-scale summer surface temperature in decades over Northern hemisphere was investigated. July-temperature in Europe, East Asia, the North Pacific, and western North America have been undergoing more increase than the global average of about 1ºC increase, especially in recent years. On the contrary, MME of 37 CMIP5 models do not show the focused regional warming hot-spots but exhibits hemispheric surface temperature increase. From the comparison between GISTEMP observation record and CMIP5 MME historical data, we show an evidence that the observed characteristic increase of regional temperature for the recent 43 years is dominated by the Internal Climate Variability (ICV) of decadal time-scale. Performing EOF analysis on the ICV, the four dominant modes are identified as Pacific Decadal Oscillation (PDO), North Pacific Oscillation (NPO), Pacific Meridional Mode (PMM), and Atlantic Multi-decadal Oscillation (AMO). It is also shown that, with only two dominant modes among those modes, large portion of continental-scale temperature increase can be explained: We show that PDO and PMM are dominant modes in Europe and East Asia, NPO and AMO in the North Pacific, and PDO and NP in western North America. The observed sub-trend is nicely reproduced with only these two modes. Quantitatively, the observed sub-trend from ICV explains 73%, 60%, 55%, and 18% of the total variability in North Pacific, East Asia, western North America and Europe, respectively. Note that, despite 82% of temperature fluctuation in Europe is attributed by the external forcing, 18% of the internal variability is still important to explain the increasing number of extreme heat events in Europe in recent several decades.

Abstract. Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared to unfiltered simulations.

To study past climate conditions, main climatic factor related to the energy consumption namely temperature and precipitation were considered. Heating degree days (HDD), cooling degree days (CDD) and sunshine hours were analyzed. According to all reports in different scales temperature of Astara region, in last half century has been increased. In the longer future time period, the study area will face with higher values of increasing temperature. HDD with base temperature of 18 °C reduced in last 18 years. HDD in January which is cold month also reduced. Meanwhile CDD increased. CDD in July which is warm month also increased. This is in harmony with increasing the number of sunshine hours in the same time slice. By changing HDD and CDD in cool and warm seasons, it means the pattern of energy consumption in the region has changed. Astara location is included in both of Central Asia and West Asia sub-regions in IPCC AR4. Projection for temperature change in Central Asia sub-region is not in agreement with national and local downscaling results.

A number of unprecedented extreme late spring frosts has recently hit Western Europe, especially France, causing severe impacts on vineyards that had already undergone bud burst. Whether climate change may have contributed to making these events more likely is poorly understood, since it depends on a subtle balance between how much the phenological cycle anticipates due to winter warming and how much cold spring nights warm. Here we shed more light on the key driving factors behind observed changes by taking a novel approach in which the different components of past warming trends, i.e. mean warming, seasonal cycle changes, diurnal temperature changes and frequency of cold snaps, are isolated and separately analysed. Bud burst date is determined using a previously tested phenological model based on growing degree days. A statistical model is introduced to modulate the different components of warming and generate thousands of sample of plausible realisations of internal climate variability. The statistical model is fit to daily mean and night minimum temperature data in 1950-2022 from the ERA5 reanalysis and EOBS. We show that in large parts of Europe even a simple seasonally and daily uniform warming leads to an increase in the risk of frosts after vineyard bud bursts. However, such simple thermodynamic effect is small compared to the observed changes in the frequency of such events. Including the effect of changes in the seasonal cycle, i.e. the winter warming amplification, and in the diurnal temperature range further increases frost risk, but it still explains only up to a third of the observed changes in the frost events. Likewise, changes in internal atmospheric variability are also insufficient to explain the observed trends. We demonstrate that only the increased frequency in an intra-seasonal  atmospheric circulation pattern causing warm winters followed by spring cold snaps, and acting on a warmed seasonal cycle, can explain the observed trends. We then analyse CMIP6 historical simulations to attribute the role of climate change versus internal climate variability. While mean warming and seasonal cycle changes are strongly influenced by climate change, the winter to spring swing in atmospheric circulation is more difficult to attribute and it may result from an extreme realisation of internal variability. We suggest that storylines can be used to illustrate plausible future risks for vineyards depending on the rate of regional warming and the type of circulation changes. 

Using tide gauge (TG) observations, we identify pronounced multidecadal fluctuations in sea level along the US Northeast Coast (USNEC) superimposed on a long-term increasing trend. This multidecadal sea level variability, largely arising from fluctuations in the buoyancy-driven Atlantic meridional overturning circulation (AMOC), substantially modulates the frequency of flood occurrences along the USNEC and serves as a source of multiyear predictability. Using an initialized dynamical downscaling decadal prediction system with a 1/12° ocean resolution, we demonstrate that flood frequency along the USNEC can be predicted on multiyear to decadal timescales. The long-term increasing trend in flood frequency, mainly driven by increasing greenhouse gases and associated radiative forcing changes, can be predicted a decade ahead. Furthermore, detrended flood frequency along the USNEC exhibits prediction skill for up to 3 years, as verified by TG observation. This multiyear prediction skill is achieved using prediction models that are initialized from our best estimate of observed AMOC.

The present paper is continuation of our recent study and analyzes the potential changes of residential heating and cooling degree-days as well as three stakeholder-relevant indices of agro-meteorological change (growing season length, sum of the active and sum of the effective temperatures) for Central and Southeast Europe over near past (1975–2004), near (2021–2050) and far (2070–2099) future periods. All indicators were calculated from the output data of our simulations with the regional climate model RegCM driven by the ERA-Interim reanalysis for the near past and by the global circulation model HadGEM2-ES under RCP8.5 CMIP5 radiative forcing scenario for the future periods. The validation of the model-based indices against their counterparts, computed from the observational dataset E-OBS, shows that the model reproduces their spatial variability and magnitude generally well. A linear bias correction of the considered indices is also demonstrated. Consistent with the general trend of the mean and extreme temperatures over the region, the study reveals a decrease of the heating degree days and considerable increase of the cooling degree days and the agro-meteorological indices practically over the whole domain in the future. The detected changes are fairly not symmetrical - the relative increase of the cooling degree days is significantly bigger than the decrease of the heating degree-days.KeywordsHeating and cooling degree-daysAgro-meteorological indicesCMIP5 RCP8.5Bias correctionRegional climate simulation

Uncertainty quantification and characterization in changing climate scenarios have a direct impact on the efforts toward mitigation and adaptation. The chaotic and nonlinear nature of atmospheric processes results in high sensitivity to initial conditions resulting in considerable variability. Multiple model ensembles of Earth System Models are often used to visualize the role of parametric uncertainties in mean and extreme attributes of precipitation trends in various time horizons. However, studies quantifying the role of internal variability in controlling extreme precipitation statistics in decadal and interdecadal scales are limited. Specifically, we quantify the relative contribution of uncertainty due to internal variability and model uncertainty in the depth and volatility of Indian Summer Monsoon rainfall extremes of different duration and frequencies. We establish that the role of internal variability in extreme precipitation indices such as 100‐years and 30‐years return levels are comparable to the uncertainty arising from structural differences in the model captured through bias‐corrected ensembles of multimodel outputs. From the regional analysis, we find that internal variability is not only comparable, but it also shows higher mean and uncertainty in estimating extreme precipitation indices in central India. The intensifying precipitation extremes have quantifiable impacts on Depth Duration Frequency (DDF) curves, which directly implicate hydraulic design and water resources planning and management. We show that combining outputs from multiple initial condition ensembles generated to span the range of internal climate variability can help us reduce uncertainty and provide the distinguished uncertainty bounds of DDF curves.

Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power

Meteorological data collected at SRS since the mid-1960’s have been analyzed for trends in minimum and maximum temperature, heating and cooling degree days, precipitation and relative humidity. The trends in meteorological data collected have been relatively small compared to the interannual variability that is observed. The observed increases, while small, appear to be real (statistically significant). Overnight low temperatures (3.1°F) have increased over twice as fast as the increases in daytime highs (1.4°F). Similarly, there are statistically significant increases in the number of cooling degree days as well. There has been a similar decrease in the number of HDD and freezing days, consistent with the overall increase in overnight low temperatures.