Downscaled Climate Projections for the Torres Strait Region: 8 km results for 2055 and 2090 (NERP TE 11.1, CSIRO)

This dataset consists of rasters representing downscaled climate change scenarios (8 km resolution) for the Torres Strait and Papua New Guinea regions for 1990, 2055, 2090. This includes estimated mean surface relative humidity (%), wind speed, rainfall rate (mm per day) and surface temperature (degrees Celsius) estimated from simulated conditions for 1980?1999, 2046-2065 and 2080?2099 time periods. Also included is the relative change of each attribute with respect to 1990.

For the past decade the Conformal Cubic Atmospheric Model (CCAM) has been the mainstay of CSIRO dynamical downscaling (McGregor 1996, 2005a, 2005b; McGregor and Dix 2001, 2008). CCAM is an atmospheric GCM formulated on the conformal-cubic grid. CCAM includes a fairly comprehensive set of physical parameterizations. The GFDL parameterizations for long-wave and short-wave radiation (Schwarzkopf and Fels 1991; Lacis and Hansen 1974) are employed, with interactive cloud distributions determined by the liquid and ice-water scheme of Rotstayn (1997). The model employs a stability-dependent boundary layer scheme based on Monin-Obukhov similarity theory (McGregor et al. 1993), together with the non-local treatment of Holtslag and Boville (1993). A canopy scheme is included, as described by Kowalczyk et al. (1994), having six layers for soil temperatures, six layers for soil moisture (solving Richard's equation) and three layers for snow. The cumulus convection scheme uses a mass-flux closure, as described by McGregor (2003), and includes downdrafts, entrainment and detrainment. CCAM is not only used for climate studies (Nguyen et al. 2011), it is also used in a short-range weather forecast system (Landman et al. 2012).

Methods:

All primary simulations were completed using CSIRO’s global stretched-grid, Conformal Cubic Atmospheric Model (CCAM; McGregor and Dix, 2008) run at 60 km horizontal resolution over the entire globe, while further downscaling to 8 km was conducted for selected partner countries. The CCAM model was chosen for the downscaling because it is a global atmospheric model, so it was possible to bias-adjust the sea-surface temperature in order to improve upon large-scale circulation patterns. In addition, the use of a stretched grid eliminates the problems caused by lateral boundary conditions in limited-area models. The model has been well tested in various model inter-comparisons and in downscaling projects over the Australasian region (Corney et al., 2010).
CCAM 60 km Global simulations:
These simulations were performed for six host global climate models (CSIRO?Mk3.5, ECHAM/MPI?OM, GFDL-CM2.0, GFDL?CM2.1, MIROC3.2 (medres) and UKMO?HadCM3) that were deemed to have acceptable skill in simulating the climate of the Pacific Climate Change Science Program region.
The period 1961-2099 was simulated for the A2 (high) emissions scenario only.

In these simulations, the sea-surface temperature bias?adjustment was calculated by computing the monthly average biases of the global models for the 1971-2000 period, relative to the observed climatology, based upon the method of Reynolds (1988). These monthly biases were then subtracted from the global climate model monthly sea-surface temperature output throughout the simulation. This approach preserves the inter- and intra-annual variability and the climate change signal of the host global climate models.

CCAM 8 km Global simulations:
Due to computational cost, only three of the CCAM 60 km global simulations (those using SSTs from GFDL-CM2.1, UKMO-HadCM3 and ECHAM5) were selected for further downscaling to 8 km. Of the six host models, these three GCM simulations showed a low, middle and high amount of global warming into the future, respectively.
A scale-selective digital filter developed by Thatcher and McGregor (2009) was used to impose the broad-scale (scales greater than approximately 500 km) fields of temperature, moisture and winds above pressure-sigma level .9 (about 1 km above the surface) from the 60 km simulations onto the 8 km simulations.

Further detail about the methods used in the development of this dataset is provided in:
Katzfey, J., Rochester, W., (2012) Downscaled Climate Projections for the Torres Strait Region: 8 km2 results for 2055 and 2090, NERP TE Milestone Report, available: http://nerptropical.edu.au/Project11.1MilestoneReport%E2%80%93May2012%E2%80%93DownscaledClimate

Limitations:

Climate change projections are inherently uncertain. The future climate will be determined by a combination of factors, including levels of greenhouse gas emissions, unexpected events (e.g. volcanic eruptions), changes in technology and energy use, and sensitivity of the climate system to greenhouse gases, as well as natural variability. Exactly how these factors will unfold is unknown. Climate models have different internal dynamics and parameterisations, and thus respond somewhat differently to the same inputs, producing a range of possible futures. This concern is partly addressed in the current study by selecting CMIP3 GCMs that reproduce current climate reasonably well, then using techniques for bias correction of SSTs that improve their representation in the current climate, but preserve the projected climate change signal and the internal variability. In addition, multi-model means of variables such as temperature and rainfall are assessed to capture the most plausible possible futures. However, the full range of future climate as projected by all GCMs should be considered as well. The best solution is to pick three cases for a given application: the worse case, the best case and the most representative (most evidence) case.

This research has revealed some new insights into the potential future climate in Torres Strait, given our current understanding. In assessing the impact of these projections, careful analysis is required. The results presented from this research are only the first step in developing a greater understanding of future climate in Torres Strait.

Format:

This dataset consists of 5 rasters (in netcdf format) for each attribute (temperature, wind speed, rainfall rate and relative humidity) consisting of 3 time periods (1990, 2055, 2090) plus relative change (1990 to 2055 and 1990 to 2090) for a total of 20 rasters files.

References:

- Corney SP, Katzfey JF, McGregor JL, Grose MR, White CJ et al (2010) Climate futures for Tasmania: climate modelling technical report. Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart
- Katzfey JJ, McGregor JL, Nguyen KC and Thatcher M (2009) Dynamical downscaling techniques: Impacts on regional climate change signals. In MODSIM09 Int. Congress on Modelling and Simulation, www.mssanz.org.au/modsim09 13:2377-2383
- McGregor JL (2005) C-CAM: Geometric aspects and dynamical formulation. CSIRO Atmospheric Research Technical Paper 43
- McGregor JL and Dix MR (2008) An updated description of the Conformal-Cubic Atmospheric Model. In: “High Resolution Simulation of the Atmosphere and Ocean”, Hamilton K and Ohfuchi W (Eds), Springer, 51–76
- Nguyen KC, Katzfey JJ, McGregor JL (2011) Global 60 km simulations with CCAM: evaluation over the tropics. Clim Dyn online first. doi:10.?1007/?s00382-011-1197-8
- Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS and Schlax MG (2007) Daily high-resolution blended analyses for sea surface temperature. J Climate 20:5473-5496

本数据集包含表征托雷斯海峡与巴布亚新几内亚区域1990年、2055年、2090年降尺度气候变化情景的栅格数据（分辨率8 km）。数据涵盖基于1980–1999年、2046–2065年及2080–2099年模拟条件估算的地表平均相对湿度（%）、风速、降水率（毫米/日）与地表温度（摄氏度），同时包含各要素相对于1990年的相对变化量。

近十年来，保形立方大气模式（Conformal Cubic Atmospheric Model, CCAM）一直是澳大利亚联邦科学与工业研究组织（Commonwealth Scientific and Industrial Research Organisation, CSIRO）动力降尺度工作的核心工具（McGregor 1996, 2005a, 2005b; McGregor and Dix 2001, 2008）。CCAM是基于保形立方网格构建的大气环流模式（Global Climate Model, GCM），集成了一套较为全面的物理参数化方案：采用GFDL长波与短波辐射参数化方案（Schwarzkopf and Fels 1991; Lacis and Hansen 1974），通过Rotstayn（1997）提出的云水与冰水方案计算交互式云分布；基于莫宁-奥布霍夫相似理论（Monin-Obukhov similarity theory）构建依赖于稳定性的边界层方案（McGregor et al. 1993），并结合Holtslag与Boville（1993）提出的非局地边界层处理方法；包含冠层方案（Kowalczyk et al. 1994），该方案设置6层土壤温度层、6层土壤湿度层（求解理查兹方程（Richard's equation））以及3层积雪层；积云对流方案采用质量通量闭合方法（McGregor 2003），涵盖下沉气流、卷挟与卷出过程。CCAM不仅应用于气候研究（Nguyen et al. 2011），还被用于短期天气预报系统（Landman et al. 2012）。

## 研究方法
所有基础模拟均采用CSIRO的全球拉伸网格保形立方大气模式（CCAM; McGregor and Dix, 2008）完成，该模式在全球范围内以60 km的水平分辨率运行，同时针对选定合作伙伴国家进一步将分辨率降尺度至8 km。选择CCAM进行降尺度的原因在于其为全球大气模式，可通过海表温度（Sea Surface Temperature, SST）偏差校正优化大尺度环流格局；此外，拉伸网格可消除区域模式侧边界条件带来的问题。该模式已在多模式对比试验以及澳大拉西亚区域的降尺度项目中得到充分验证（Corney et al., 2010）。

### CCAM 60 km全球模拟
本次模拟共选取6个在太平洋气候变化科学计划区域气候模拟中表现合格的宿主全球气候模式，分别为CSIRO-Mk3.5、ECHAM/MPI-OM、GFDL-CM2.0、GFDL-CM2.1、MIROC3.2（medres）与UKMO-HadCM3。模拟时段为1961–2099年，仅考虑A2（高）排放情景。
在模拟过程中，海表温度偏差校正通过以下方式实现：以Reynolds（1988）提出的方法为基础，计算1971–2000年时段内各全球模式相对于观测气候态的月平均偏差，随后在整个模拟时段内将该月偏差从全球气候模式的海表温度月输出结果中扣除。该方法可保留宿主全球气候模式的年际与年内变率以及气候变化信号。

### CCAM 8 km全球模拟
受计算成本限制，仅选取3个CCAM 60 km全球模拟结果（分别采用GFDL-CM2.1、UKMO-HadCM3与ECHAM5的海表温度场）进一步降尺度至8 km分辨率。在6个宿主模式中，这3个全球环流模式模拟的未来全球变暖幅度分别对应低、中、高三个等级。
采用Thatcher与McGregor（2009）开发的尺度选择性数字滤波器，将60 km模拟结果中气压σ层0.9（约地表以上1 km）以上的大尺度（尺度约大于500 km）温度、水汽与风场叠加至8 km模拟结果中。

本数据集开发方法的更多细节可参考：Katzfey, J., Rochester, W., (2012) 《托雷斯海峡降尺度气候预估：8 km²分辨率2055年与2090年结果》，NERP TE里程碑报告，可获取地址：http://nerptropical.edu.au/Project11.1MilestoneReport%E2%80%93May2012%E2%80%93DownscaledClimate

## 数据局限性
气候变化预估本质上具有不确定性。未来气候由多种因素共同决定，包括温室气体排放水平、突发事件（如火山喷发）、技术与能源使用变化、气候系统对温室气体的敏感性以及自然变率，上述因素的未来演变路径均未知。不同气候模式具有不同的内部动力过程与参数化方案，因此对相同输入的响应存在差异，进而产生一系列可能的未来情景。本研究通过以下方式部分缓解该问题：选取能够较好模拟当前气候的第三次耦合模式比较计划（Coupled Model Intercomparison Project Phase 3, CMIP3）全球环流模式，采用海表温度偏差校正技术优化其对当前气候的模拟效果，同时保留预估的气候变化信号与内部变率；此外，通过评估温度、降水等要素的多模式集合平均结果，以获取最具合理性的未来情景。但仍需考虑所有全球环流模式预估的完整未来气候范围。最佳实践方案为针对特定应用选取三类情景：最差情景、最优情景与最具代表性（证据最充分）情景。
基于当前认知，本研究揭示了托雷斯海峡未来气候的若干新潜在特征。在评估这些预估结果的影响时，需进行细致分析。本研究所得结果仅是加深对托雷斯海峡未来气候理解的第一步。

## 数据格式
本数据集针对每个要素（温度、风速、降水率与相对湿度）各包含5个网络通用数据格式（NetCDF）栅格文件，对应3个时间节点（1990年、2055年、2090年）以及2个相对变化时段（1990–2055年与1990–2090年），总计20个栅格文件。

## 参考文献
- Corney SP, Katzfey JF, McGregor JL, Grose MR, White CJ et al (2010) 《塔斯马尼亚气候展望：气候模拟技术报告》，南极气候与生态系统合作研究中心，霍巴特
- Katzfey JJ, McGregor JL, Nguyen KC and Thatcher M (2009) 《动力降尺度技术：对区域气候变化信号的影响》，载于MODSIM09国际建模与模拟大会，www.mssanz.org.au/modsim09 13:2377-2383
- McGregor JL (2005) 《C-CAM：几何特征与动力表述》，CSIRO大气研究技术报告43
- McGregor JL and Dix MR (2008) 《保形立方大气模式的更新说明》，载于《大气与海洋高分辨率模拟》，Hamilton K与Ohfuchi W（编辑），Springer，51–76
- Nguyen KC, Katzfey JJ, McGregor JL (2011) 《CCAM全球60 km模拟：热带区域评估》，《气候动力学》在线优先出版，doi:10.1007/s00382-011-1197-8
- Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS and Schlax MG (2007) 《每日高分辨率融合海表温度分析》，《气候学报》20:5473-5496