ICARIA: spatially distributed climate projections from statistical downscaling

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose of being freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset, being obtained from a high-quality and high-density set of weather observations that are then interpolated to the case studies of interest in a 100x100m resolution grid, which is the main outcome offered in this publication. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes. For further details, find here a brief of the methodology followed: ----- The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required. For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach. For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”. For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs. This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future. ----- The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI). CMIP6 MODELS Resolution Responsible Centre References ACCESS-CM2 1,875º x 1,250º Australian Community Climate and Earth System Simulator (ACCESS), Australia Bi, D. et al (2020) BCC-CSM2-MR 1,125º x 1,121º Beijing Climate Center (BCC), China Meteorological Administration, China. Wu T. et al. (2019) CanESM5 2,812º x 2,790º Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. Swart, N.C. et al. (2019) CMCC-ESM2 1,000º x 1,000º Centro Mediterraneo sui Cambiamenti Climatici (CMCC). Cherchi et al, 2018 CNRM-ESM2-1 1,406º x 1,401º CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. Seferian, R. (2019) EC-EARTH3 0,703º x 0,702º EC-EARTH Consortium EC-Earth Consortium. (2019) MPI-ESM1-2-HR 0,938º x 0,935º Max-Planck Institute for Meteorology (MPI-M), Germany. Müller et al., (2018) MRI-ESM2-0 1,125º x 1,121º Meteorological Research Institute (MRI), Japan. Yukimoto, S. et al. (2019) NorESM2-MM 1,250º x 0,942º Norwegian Climate Centre (NCC), Norway. Bentsen, M. et al. (2019) UKESM1-0-LL 1,875º x 1,250º UK Met Office, Hadley Centre, United Kingdom Good, P. et al. (2019) The climate projections have been developed over each of the observational locations that were retrieved to run the statistical downscaling. The results from these projections have been spatially interpolated into a 100x100m grid with a Multi-lineal Regression Model considering diverse adjustments and topographic corrections. The results presented here are the median of the 10 models used, obtained for each of the 4 SSPs and each of the time periods considered in ICARIA until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections. Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. Index/name Short description Source Variable Units Threshold Thermal indicators TX90 / TX10 Warm/cold days Zhang et al. (2011) TX nd 90 / 10% HD Heat day ICARIA TX nd > 30 °C EHD Extreme heat day ICARIA TX nd > 35 °C TR Tropical nights Zhang et al. (2011) TN nd > 20 °C EQ Equatorial nights AEMet 2020, ICARIA TN nd > 25 °C IN Infernal nights ICARIA TN nd > 30 °C FD Frost days Zhang et al. (2011) TN nd < 0 °C Max consec Max spell length for above thermal indicators ICARIA - nd - Nº events Number of above thermal indicators events ICARIA - ne > 3 days TXm Mean maximum temperatures ICARIA TX °C - TNm Mean minimum temperatures ICARIA TN °C - TM Mean temperatures ICARIA TA °C - HWle Heatwave length ICARIA TX nd 3d > 95% TX HWim/HWix Mean and maximum heatwave intensity ICARIA TX °C 3d > 95% TX HWf Heatwave frequency ICARIA TX ne 3d > 95% TX HWd Heatwave days ICARIA TX nd 3d > 95% TX HI - P90 Heat Index (percentile 90) NWS (1994) TX, RH °C TX>27 °C, HR> 40% UTCI Universal Thermal Climate Index Bröde et al. (2012) TARH, W - - UHI Isla de calor (BCN) anual y estacional AMB, Metrobs 2015 T °C TM1-TM2 > 0 °C Precipitation indicators R20 Number of heavy precipitation days Zhang et al. (2011) P nd >20 mm R50, R100 Days with extreme heavy rain AMB et al. (2017) P nd >50mm >100mm Ra Yearly and seasonal rainfall relative change ICARIA P mm ≥ 0.1mm IDF - CCF IDF Curves - Climate Change Factor Arnbjerg-Nielsen (2012) P - ≥ 0.1mm Forest fire indicators Mean FWI Mean Canadian FWI in fire season Stock, B.J. et al. (1989) RHn, TX, P, W . June-September Very High FWI Very High Canadian FWI Stock, B.J. et al. (1989) RHn, TX, P, W nd FWI > 38 Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. Index/name Short description Source Variable Units Threshold Drought indicators CDDx Maximum dry spell duration Zhang et al. (2011) P nd < 1 mm CDDm Mean dry spell duration Zhang et al. (2011) P nd < 1 mm SPI SPI of 1, 3, 6, 12, 24 & 36 months McKee et al. (1993) P, TA mm ≥ 0.1mm SPEI SPEI of 1, 3, 6, 12, 24 & 36 months Vicente-Serrano et al. (2010) P, TA mm ≥ 0.1mm Oceanic indicators SS Storm surge Bryant et al. (2016) MT cm - OW Significant/maximum wave height ICARIA WH m - Wind indicators EWG Extreme wind gusts ICARIA W km/h -ICARIA项目的核心目标之一，是基于最新的第六次耦合模式比较计划（Coupled Model Intercomparison Project 6, CMIP6）数据，构建一致、可靠且可复用的降尺度气候预估结果，为相关利益相关者的气候适应与决策提供高效支撑，并助力项目范围内关键资产的气候适应工作。本次气候预估结果的另一研发目标，是实现免费开放，以供后续研究进一步使用，进而推动更多区域的气候变化适应工作。因此，ICARIA项目的气候信息以CMIP6模式为基础，并在工作流程中纳入了当前的共享社会经济路径（Shared Socioeconomic Pathways, SSPs）。 本研究呈现的高分辨率未来气候预估结果构成了一套独特数据集：其研发依托高质量、高密度的气象观测数据集，并通过插值得到100米×100米分辨率的目标研究区域网格数据，这也是本论文发布的核心成果。上述模式将为风险评估以及ICARIA项目所有适配措施的设计与开发提供所需的情景支撑。 如需了解更多细节，以下为所采用方法的简要概述： 统计降尺度方法 ICARIA项目中由FIC采用的统计降尺度方法名为FICLIMA（Ribalaygua等，2013），该方法属于两步式相似-回归统计方法，已在多项国内外项目中应用并取得良好验证效果（如Monjo等，2016）。 第一步适用于所有模拟气候变量，基于相似分层法（Zorita等，1993）。该相似方法的核心假设为：相似的大气环流型（预报因子，predictors）会引发相似的局地气候响应（预报量，predictands），即选取与待降尺度日最为相似的若干日期。任意两日的相似度通过三个嵌套的天气尺度窗口（赋予不同权重）以及四个大尺度场进行衡量，采用以大尺度场为预报因子的伪欧氏距离计算。针对每个预报因子，先计算加权欧氏距离，再通过将其替换为该预报因子加权欧氏距离参考总体的最邻近百分位数完成标准化。该方法能够较好地复现预报因子与预报量之间的非线性关系，但无法模拟观测值范围之外的数值。为解决这一问题并获得更优的模拟效果，需开展第二步处理。 第二步的具体处理流程取决于目标气候变量： 1. 气温变量：针对每个气象站点以及每个待降尺度日，基于选取的最相似日期集合开展多元线性回归分析。通过向前-向后逐步回归法，从潜在预报因子集合中筛选出相关性最高的因子。 2. 降水变量：将m个待降尺度日（此处采用整月所有日期）进行降尺度处理。对每个待降尺度日，通过对其n个最相似日期的降雨量取平均，得到"初步降水量"，随后将m个待降尺度日按"初步降水量"从高到低排序。为确定最终降水量，先将所有m×n个相似日期的降水量进行排序并划分为m个组，再按顺序将各组降水量分配给此前按"初步降水量"排序的m个待降尺度日。 3. 风速或相对湿度变量：第二步采用基于n=30个相似日期平均值构建的传递函数，实现观测概率分布与模拟概率分布的匹配。具体而言，先对第一步相似分层得到的时间序列开展参数化偏差校正。为评估该处理流程的改进效果，研究同时将偏差校正应用于模式直接输出结果。 第二步以日尺度开展，并内嵌了完整的验证流程，这是FICLIMA方法的核心与差异化优势所在。该方法不仅能够模拟气候变量的均值特征，还可覆盖极端值，并适配包括日尺度在内的所有时间尺度。通过验证可确认该方法能否准确模拟逐日气候变化，这意味着其能够有效捕捉预报因子与预报量之间的内在物理联系。即便在气候变化背景下，这些物理联系仍能保持相对稳定（区别于可能发生偏移的纯经验关系）。从本质上讲，该方法从理论上解决了统计降尺度领域的核心挑战——非平稳性问题。该问题质疑过往建立的预报因子-预报量关系的稳定性，探究这些关系在未来是否依然成立。 数据集概况 本次共享的数据集涵盖ICARIA项目的三个研究案例区域：巴塞罗那都会区（Barcelona Metropolitan Area, AMB）、萨尔茨堡州（Salzburg Region, SLZ）以及南爱琴海大区（South Aegean Region, SAR）。数据集包含10个CMIP6模式的相关数据与预估结果。每个模式均包含1950年1月1日至2014年12月31日的历史模拟数据，以及2015年1月1日至2100年12月31日的4个未来情景（ssp126、ssp245、ssp370与ssp585）模拟结果。所选模式的详细信息见下表： 表1 对应IPCC第六次评估报告（AR6）的10个CMIP6气候模式信息。数据从地球系统网格联合会（Earth System Grid Federation, ESGF）门户获取，以支持气候模式诊断与比较计划（Program for Climate Model Diagnosis and Intercomparison, PCMDI）的相关工作。 | CMIP6模式 | 分辨率 | 负责机构 | 参考文献 | | --- | --- | --- | --- | | ACCESS-CM2 | 1.875°×1.250° | 澳大利亚社区气候与地球系统模拟器（ACCESS）团队，澳大利亚 | Bi, D.等（2020） | | BCC-CSM2-MR | 1.125°×1.121° | 中国气象局北京气候中心（BCC），中国 | Wu T.等（2019） | | CanESM5 | 2.812°×2.790° | 加拿大气候模拟与分析中心（CC-CMA），加拿大 | Swart, N.C.等（2019） | | CMCC-ESM2 | 1.000°×1.000° | 地中海气候变化研究中心（CMCC） | Cherchi等（2018） | | CNRM-ESM2-1 | 1.406°×1.401° | 法国国家气象研究中心（CNRM）、法国气象局，法国 | Seferian, R.（2019） | | EC-EARTH3 | 0.703°×0.702° | EC-EARTH联盟 | EC-Earth Consortium（2019） | | MPI-ESM1-2-HR | 0.938°×0.935° | 德国马克斯·普朗克气象研究所（MPI-M） | Müller等（2018） | | MRI-ESM2-0 | 1.125°×1.121° | 日本气象研究所（MRI） | Yukimoto, S.等（2019） | | NorESM2-MM | 1.250°×0.942° | 挪威气候中心（NCC），挪威 | Bentsen, M.等（2019） | | UKESM1-0-LL | 1.875°×1.250° | 英国气象局哈德利中心，英国 | Good, P.等（2019） | 本次气候预估针对所有用于统计降尺度的观测站点开展，预估结果通过多元线性回归模型进行空间插值，生成100米×100米分辨率的网格数据，插值过程中考虑了多种校正与地形修正。本研究呈现的结果为10个模式的中位数结果，覆盖4个SSP情景以及ICARIA项目中截至2100年的所有研究时段。所涉及的变量包括ICARIA项目中定义的主要气候变量及其相关极端气候指标。所有用于构建预估结果的变量与指标的汇总信息见下表： 表2 所选热相关与降水相关指标汇总，按其关联的主要灾害类型分组。"nd"为天数；"ne"为事件数。 | 指标/名称 | 简要描述 | 来源 | 变量 | 单位 | 阈值 | | --- | --- | --- | --- | --- | --- | | **热相关指标** | | | | | | | TX90 / TX10 | 暖日/冷日 | Zhang等（2011） | TX | nd | 90% / 10% | | HD | 高温日 | ICARIA | TX | nd | >30℃ | | EHD | 极端高温日 | ICARIA | TX | nd | >35℃ | | TR | 热带夜 | Zhang等（2011） | TN | nd | >20℃ | | EQ | 赤道夜 | AEMet 2020、ICARIA | TN | nd | >25℃ | | IN | 极端闷热夜 | ICARIA | TN | nd | >30℃ | | FD | 霜日 | Zhang等（2011） | TN | nd | <0℃ | | Max consec | 上述热指标的最长持续天数 | ICARIA | - | nd | - | | Nº events | 上述热指标的事件数 | ICARIA | - | ne | >3天 | | TXm | 平均最高气温 | ICARIA | TX | ℃ | - | | TNm | 平均最低气温 | ICARIA | TN | ℃ | - | | TM | 平均气温 | ICARIA | TA | ℃ | - | | HWle | 热浪持续时长 | ICARIA | TX | nd | 3d > 95% TX | | HWim/HWix | 热浪平均/最大强度 | ICARIA | TX | ℃ | 3d > 95% TX | | HWf | 热浪发生频率 | ICARIA | TX | ne | 3d > 95% TX | | HWd | 热浪日数 | ICARIA | TX | nd | 3d > 95% TX | | HI - P90 | 热指数（90百分位） | NWS（1994） | TX、RH | ℃ | TX>27℃、RH>40% | | UTCI | 通用热气候指数 | Bröde等（2012） | TARH、W | - | - | | UHI | 巴塞罗那（BCN）年度与季节热岛 | AMB、Metrobs 2015 | T | ℃ | TM1-TM2>0℃ | | **降水相关指标** | | | | | | | R20 | 强降水日数 | Zhang等（2011） | P | nd | >20mm | | R50、R100 | 极端强降水日数 | AMB等（2017） | P | nd | >50mm、>100mm | | Ra | 年度与季节降雨量相对变化 | ICARIA | P | mm | ≥0.1mm | | IDF - CCF | IDF曲线-气候变化因子 | Arnbjerg-Nielsen（2012） | P | - | ≥0.1mm | | **森林火险指标** | | | | | | | Mean FWI | 火险季平均加拿大火天气指数 | Stock, B.J.等（1989） | RHn、TX、P、W | - | 6-9月 | | Very High FWI | 极端加拿大火天气指数 | Stock, B.J.等（1989） | RHn、TX、P、W | nd | FWI>38 | 表3 所选干旱、海洋与风速指标汇总，按其关联的主要灾害类型分组。"nd"为天数；"ne"为事件数。 | 指标/名称 | 简要描述 | 来源 | 变量 | 单位 | 阈值 | | --- | --- | --- | --- | --- | --- | | **干旱指标** | | | | | | | CDDx | 最长干旱持续天数 | Zhang等（2011） | P | nd | <1mm | | CDDm | 平均干旱持续天数 | Zhang等（2011） | P | nd | <1mm | | SPI | 1、3、6、12、24及36个月尺度标准化降水指数 | McKee等（1993） | P、TA | mm | ≥0.1mm | | SPEI | 1、3、6、12、24及36个月尺度标准化降水蒸散指数 | Vicente-Serrano等（2010） | P、TA | mm | ≥0.1mm | | **海洋指标** | | | | | | | SS | 风暴潮 | Bryant等（2016） | MT | cm | - | | OW | 有效/最大波高 | ICARIA | WH | m | - | | **风速指标** | | | | | | | EWG | 极端阵风 | ICARIA | W | km/h | - |