Changes in monthly mean clod amount over China: A closer look

Clouds play a primary role in governing the heat balance of the earth-atmosphere system. Analysis of historical cloudiness data is important in attempts to understand the nature of past climate changes and potential future changes. The studies by Kaiser (1993) and Kaiser and Vose (1994) (hereafter referred to as KKV) do show some evidence of significant regional and seasonal changes. In KKV, monthly records of cloud amount from 60 stations, made available to the US Department of Energy`s (DOE`s) Carbon Dioxide Information Analysis Center (CDIAC) through an agreement with the Chinese Academy of Sciences (CAS) were analyzed for trends over the period 1954--88. Since the work of KKV, much more historical weather data have been made available to CDIAC via a recently established research agreement between DOE and the Chinese Meteorological Administration (CMA). Here the authors use some of these data to take a closer look at cloudiness changes over China in recent decades

Changing state of the climate system

Chapter 2 assesses observed large-scale changes in climate system drivers, key climate indicators and principal modes of variability. Chapter 3 considers model performance and detection/attribution, and Chapter 4 covers projections for a subset of these same indicators and modes of variability. Collectively, these chapters provide the basis for later chapters, which focus upon processes and regional changes. Within Chapter 2, changes are assessed from in situ and remotely sensed data and products and from indirect evidence of longer-term changes based upon a diverse range of climate proxies. The time-evolving availability of observations and proxy information dictate the periods that can be assessed. Wherever possible, recent changes are assessed for their significance in a longer-term context, including target proxy periods, both in terms of mean state and rates of change

Genetic algorithm in ab initio protein structure prediction using low resolution model : a review

Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution

Reply

We strongly welcome the input of Dee and colleagues (Dee et al. 2010) on our recent essay piece (Thorne and Vose 2010). The original essay was deliberately written as ideas for discussion. Dee et al. make many valid points. We feel in particular that Dee et al.’s laying out of plans for the data they plan to publish along with their next major reanalysis is a positive step. Too often such plans are not publicized in this way in advance of project inception. We restrict our discussion within the following sections to those points where we either need to clarify our initial intent or wish to comment on the points raised by Dee et al. Before that, there are two general issues that are not of scientific focus that we would like to clarify

Reanalyses Suitable for Characterizing Long-Term Trends Are They Really Achievable?

Reanalyses are, by a substantial margin, the most utilized climate data products, and they are applied in a myriad of different contexts. Despite their popularity, there are substantial concerns about their suitability for the monitoring of long-term climate trends. This has led to calls for a truly “climate quality” reanalysis that retains long-term trend fidelity. The authors contend that for such a reanalysis to be achieved, a substantial rethinking of the current strategy for producing reanalysis products is required. First, the problem must be defined clearly. Second, the methodology that is employed must be reconsidered so as to minimize potential non-climatic artifacts and robustly ascertain the inevitable residual uncertainty. Finally, a set of validation data and metrics must be constructed that the community can use to compare and unambiguously assess the claims of climate quality. The purpose of this essay is very much to initiate discussions to this end rather than to prescribe solutions

An intercomparison of temperature trends in the U.S. Historical Climatology Network and recent atmospheric reanalyses

Temperature trends over 1979–2008 in the U.S. Historical Climatology Network (HCN) are compared with those in six recent atmospheric reanalyses. For the conterminous United States, the trend in the adjusted HCN (0.327 °C dec−1) is generally comparable to the ensemble mean of the reanalyses (0.342 °C dec−1). It is also well within the range of the reanalysis trend estimates (0.280 to 0.437 °C dec−1). The bias adjustments play a critical role, as the raw HCN dataset displays substantially less warming than all of the reanalyses. HCN has slightly lower maximum and minimum temperature trends than those reanalyses with hourly temporal resolution, suggesting the HCN adjustments may not fully compensate for recent non-climatic artifacts at some stations. Spatially, both the adjusted HCN and all of the reanalyses indicate widespread warming across the nation during the study period. Overall, the adjusted HCN is in broad agreement with the suite of reanalyses

Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons

The monthly global 28 3 28 Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.0778C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.18–0.28C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4

Reassessing changes in diurnal temperature range: A new data set and characterization of data biases

It has been a decade since changes in diurnal temperature range (DTR) globally have been assessed in a stand-alone data analysis. The present study takes advantage of substantively improved basic data holdings arising from the International Surface Temperature Initiative’s databank effort and applies the National Centers for Environmental Information’s automated pairwise homogeneity assessment algorithm to reassess DTR records. It is found that breakpoints are more prevalent in DTR than other temperature elements and that the resulting adjustments have a broader distribution. This strongly implies that there is an overarching tendency, across the global meteorological networks, for nonclimatic artifacts to impart either random or anticorrelated rather than correlated biases in maximum and minimum temperature series. Future homogenization efforts would likely benefit from simultaneous consideration of DTR and maximum and minimum temperatures, in addition to average temperatures. Estimates of change in DTR are relatively insensitive to whether adjustments are calculated directly or inferred from adjustments returned for the maximum and minimum temperature series. The homogenized series exhibit a reduction in DTR since the midtwentieth century globally (-0.044 K/decade). Adjustments serve to approximately halve the long-term global reduction in DTR in the basic “raw” data. Most of the estimated DTR reduction occurred over 1960–1980. In several regions DTR has apparently increased over 1979–2012, while globally it has exhibited very little change (-0.016 K/decade). Estimated changes in DTR are an order of magnitude smaller than in maximum and minimum temperatures, which have both been increasing rapidly on multidecadal timescales (0.186 K/decade and 0.236 K/decade, respectively, since the midtwentieth century)