What spatial scales are believable for climate model projections of sea surface temperature?

Earth system models (ESMs) provide high resolution simulations of variables such as sea surface temperature (SST) that are often used in off-line biological impact models. Coral reef modellers have used such model outputs extensively to project both regional and global changes to coral growth and bleaching frequency. We assess model skill at capturing sub-regional climatologies and patterns of historical warming. This study uses an established wavelet-based spatial comparison technique to assess the skill of the coupled model intercomparison project phase 5 models to capture spatial SST patterns in coral regions. We show that models typically have medium to high skill at capturing climatological spatial patterns of SSTs within key coral regions, with model skill typically improving at larger spatial scales (≥4°). However models have much lower skill at modelling historical warming patters and are shown to often perform no better than chance at regional scales (e.g. Southeast Asian) and worse than chance at finer scales (<8°). Our findings suggest that output from current generation ESMs is not yet suitable for making sub-regional projections of change in coral bleaching frequency and other marine processes linked to SST warming.     

A macro-scale flood risk model for Jamaica with impact of climate variability

Floods account for more than half of the global hydrometeorological risks. Severe floods cause significant economic shocks and loss of lives, particularly for developing countries such as Jamaica. There is need for more information on the present and projected flood risks to justify macro-scale planning for climate change adaptation and facilitate the decision-making processes. In this study, a catalogue of 198 flood events occurring between 1678 and 2010 is compiled for Jamaica and used to examine the climatology, occurrence, trends, causes and duration of the island’s severe events. The annual flood risk is estimated to be a loss of life rate of 4 persons and estimated annual damage of USD96.3 million per annum in 2010 values and approximately 0.84 % of GDP per annum. Macro-scale models for flood risks (deaths and damages) are also developed using data from the flood catalogue and maximum precipitation at the town and parish level. The models examine the relationship between flood risks (death and damages) and extreme rainfall depths and intensities. Future climate risks of loss of lives and damages are predicted to increase 11 and 9 %, respectively, to 4.4 persons and USD105.2 million per annum.     

A verification framework for interannual-to-decadal predictions experiments

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.     

Comment on Varshni's recent paper on quasar red shifts

A recent paper (Varshni, 1976) analyses the distribution of quasar red shifts for randomness, in an incorrect manner. A correct analysis shows that this distribution is in agreement with random expectation. Were the distribution highly non-random, the original conclusion was that, for the red shifts to be cosmological, the Earth would have a strongly privileged position in the Universe. A simple alternative model, in which this would not be so, is pointed out.     

Higher precision estimates of regional polar warming by ensemble regression of climate model projections

This study presents projections of twenty-first century wintertime surface temperature changes over the high-latitude regions based on the third Coupled Model Inter-comparison Project (CMIP3) multi-model ensemble. The state-dependence of the climate change response on the present day mean state is captured using a simple yet robust ensemble linear regression model. The ensemble regression approach gives different and more precise estimated mean responses compared to the ensemble mean approach. Over the Arctic in January, ensemble regression gives less warming than the ensemble mean along the boundary between sea ice and open ocean (sea ice edge). Most notably, the results show 3?°C less warming over the Barents Sea (~7?°C compared to ~10?°C). In addition, the ensemble regression method gives projections that are 30?% more precise over the Sea of Okhostk, Bering Sea and Labrador Sea. For the Antarctic in winter (July) the ensemble regression method gives 2?°C more warming over the Southern Ocean close to the Greenwich Meridian (~7?°C compared to ~5?°C). Projection uncertainty was almost half that of the ensemble mean uncertainty over the Southern Ocean between 30° W to 90° E and 30?% less over the northern Antarctic Peninsula. The ensemble regression model avoids the need for explicit ad hoc weighting of models and exploits the whole ensemble to objectively identify overly influential outlier models. Bootstrap resampling shows that maximum precision over the Southern Ocean can be obtained with ensembles having as few as only six climate models.     

Using the Newly‐created ILE DBMS to Better Represent Temporal and Historical GIS Data

This article introduces a type of DBMS called the Intentionally‐Linked Entities (ILE) DBMS for use as the basis for temporal and historical Geographical Information Systems. ILE represents each entity in a database only once, thereby mostly eliminating redundancy and fragmentation, two major problems in Relational and other database systems. These advantages of ILE are realized by using relationship objects and pointers to implement all of the relationships among data entities in a native fashion using dynamically‐allocated linked data structures. ILE can be considered to be a modern and extended implementation of the E/R data model. ILE also facilitates storage of things that are more faithful to the historical records, such as gazetteer entries of places with imprecisely known or unknown locations. This is difficult in Relational database systems but is a routine task using ILE because ILE is implemented using modern memory allocation techniques. We use the China Historical GIS (CHGIS) and other databases to illustrate the advantages of ILE. This is accomplished by modeling these databases in ILE and comparing them to the existing Relational implementations.     

南海张裂过程及其对晚中生代以来东南亚构造的启示——IODP建议书735-Full介绍

南海的形成揭示了大陆边缘张裂和盆地形成的复杂模式,尽管已经进行了广泛研究,但是关于基底岩石和深海盆沉积层的精确年代数据还很缺乏,这使得对南海张裂年代的估计存在很大的误差,对张裂机制和历史的各种假设没有得到验证.同时只有对南海的张裂过程有了精确地分析与刻画,才能更好地理解西太平洋边缘海盆地的形成以及它们在印支块体受印度-欧亚板块碰撞而向东南挤出、青藏高原隆升中可能起到的作用.2009年正式提交的国际综合大洋钻探计划(IODP)建议书735-Full建议在南海深海盆内的4个站位上实施钻探.这4个站位分布在南海盆地4个不同的次级构造单元上(南海东北部、西北次海盆、东部次海盆和西南次海盆),这样的站位设计会确保完成本建议书的整体研究目标,即揭示南海的张裂历史和它对晚中生代以来东南亚构造的启示.位于南海盆地最东北部的站位有助于确定该区域地壳的属性和验证古南海是否存在,位于西北次海盆的站住可能会提供南海的最早张裂年代,另外2个分别位于东部次海盆和西南次海盆的站位将重点确定2个次海盆的绝对年龄、基底矿物成分与磁化率以及2个次海盆的相对张裂次序.这些站位的水深大约在2 910～4 400 m,钻探深度预计到海底以下大约700～2 200 m,总的钻透深度为5 959 m,其中5 359 m穿透沉积层,另外600 m或400 m钻入基底.所有这些站位的位置是由已有的地球物理观测数据所确定,目前计划收集更多的地质与地球物理数据以满足IODP对井位调查数据的要求.