ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society

The ERA-Interim reanalysis: configuration and performance of the data assimilation system

The ERA5 global reanalysis

ERA-40 is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re-analysis period, with assimilable data provided by a succession of satellite-borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean-buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA-40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA-40. This benefited from many of the changes introduced into operational forecasting since the mid-1990s, when the systems used for the 15-year ECMWF re-analysis (ERA-15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized.

A number of results indicative of the overall performance of the data assimilation system, and implicitly of the observing system, are presented and discussed. The comparison of background (short-range) forecasts and analyses with observations, the consistency of the global mass budget, the magnitude of differences between analysis and background fields and the accuracy of medium-range forecasts run from the ERA-40 analyses are illustrated. Several results demonstrate the marked improvement that was made to the observing system for the southern hemisphere in the 1970s, particularly towards the end of the decade. In contrast, the synoptic quality of the analysis for the northern hemisphere is sufficient to provide forecasts that remain skilful well into the medium range for all years. Two particular problems are also examined: excessive precipitation over tropical oceans and a too strong Brewer-Dobson circulation, both of which are pronounced in later years. Several other aspects of the quality of the re-analyses revealed by monitoring and validation studies are summarized. Expectations that the ‘second-generation’ ERA-40 re-analysis would provide products that are better than those from the firstgeneration ERA-15 and NCEP/NCAR re-analyses are found to have been met in most cases. © Royal Meteorological Society, 2005. The contributions of N. A. Rayner and R. W. Saunders are Crown copyright.

The ERA‐40 re‐analysis

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

/pdf/identification-and-analysis-of-functional-elements-in-1-of-2o640l8ikl.pdf

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coher- ence for examining relationships in time frequency space be- tween two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relation- ships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (http://www.pol.ac.uk/home/research/waveletcoherence/). As we are interested in extracting low s/n ratio signals in time series we discuss only CWT in this paper. While CWT is a common tool for analyzing localized intermittent os- cillations in a time series, it is very often desirable to ex- amine two time series together that may be expected to be linked in some way. In particular, to examine whether re- gions in time frequency space with large common power have a consistent phase relationship and therefore are sug- gestive of causality between the time series. Many geophys- ical time series are not Normally distributed and we suggest methods of applying the CWT to such time series. From two CWTs we construct the Cross Wavelet Transform (XWT) which will expose their common power and relative phase in time-frequency space. We will further define a measure of Wavelet Coherence (WTC) between two CWT, which can find significant coherence even though the common power is low, and show how confidence levels against red noise back- grounds are calculated. We will present the basic CWT theory before we move on to XWT and WTC. New developments such as quanti- fying the phase relationship and calculating the WTC sig- nificance level will be treated more fully. When using the methods on time series it is important to have solid mecha- nistic foundations on which to base any relationships found, and we caution against using the methods in a "scatter-gun" approach (particularly if the time series probability density functions are modified). To illustrate how the various meth- ods are used we apply them to two data sets from meteo- rology and glaciology. Finally, we will provide links to a MatLab software package.

/pdf/application-of-the-cross-wavelet-transform-and-wavelet-3aorulqrv4.pdf

Application of the cross wavelet transform and wavelet coherence to geophysical time series

/pdf/predictability-of-anomalous-storm-tracks-4j37jnnae3.pdf

Predictability of Anomalous Storm Tracks

Extreme wind events are among the costliest natural disasters in Europe. Significant effort is dedicated to understanding the risk of such events, usually analysing observed storms in the modern era. However, it is likely that some historical windstorms were more extreme and/or followed different tracks from those in the modern era. Producing plausible reanalyses of such events would improve the quantification of current and future windstorm risks.Billions of historical climatological observations remain unavailable to science as they exist only on paper, stored in numerous archives around the world. We demonstrate how the rescue of such paper observations has improved our understanding of an extreme windstorm that occurred in February 1903 and its significant impacts. By assimilating newly rescued atmospheric pressure observations into the 20th Century Reanalysis system, the storm is now credibly represented in an improved reanalysis of the event. In some locations this storm produced stronger winds than any event during the modern era. As a result, estimates of risk from severe storms, based on modern period data, may need to be revised. Simulations of the storm surge resulting from this storm show a large coastal surge of around 2.5m, comparing favourably with newly rescued tide gauge observations and increasing our confidence in the quality of the reconstruction.In addition, we use novel reanalysis experiments to translate this windstorm into a warmer world to quantify how it might be different both in the present and in the future. We find that the same storm produces more intense rainfall and stronger winds in a warmer climate, providing a new approach to quantifying how extreme weather events will change as the world is warming. 

Quantifying windstorm risks by translating historical extreme events into the future

The Madden‐Julian oscillation (MJO) significantly impacts North Atlantic hurricanes, with increased hurricane activity occurring when the MJO enhances convection over Africa and the tropical Indian Ocean and suppressed hurricane activity occurring when the MJO enhances convection over the tropical Pacific. Using data from 1905 to 2015, we find more tropical cyclones (TCs) make landfall in the continental United States when the MJO enhances tropical Indian Ocean convection. In addition, when the MJO enhances Western Pacific and Western Hemisphere convection, TC activity is preferentially favored in the Caribbean, leading to more Gulf Coast landfalls. As MJO‐enhanced convection moves to the Indian Ocean and Maritime Continent, more storms form in the tropical Atlantic, favoring Florida Peninsula and East Coast landfalls. The MJO's TC steering wind modulation appears to be secondary to its genesis location modulation.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2023GL102762

Influence of the Madden‐Julian Oscillation on Continental United States Hurricane Landfalls

Abstract. A new reanalysis-based approach is proposed to examine how extreme weather events differ in a warmer or cooler counter-factual world. This approach offers a novel way to develop plausible storylines for some types of extreme event that other methods may not be suitable for. As a proof-of-concept, a reanalysis of a severe windstorm that occurred in February 1903 is translated into a warmer world where it produces higher wind speeds and increased rainfall, suggesting that this storm would be more damaging if it occurred today rather than 120 years ago. 

ESD Ideas: Translating historical extreme weather events into a warmer world

State‐of‐the‐art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse‐input reanalysis products which are constrained by observed sea surface temperatures, sea‐ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse‐input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour‐based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long‐term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post‐2015 when the NOAA‐CIRES‐DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases.

Gilbert P. Compo

Papers

Predictability of Anomalous Storm Tracks

Quantifying windstorm risks by translating historical extreme events into the future

Influence of the Madden‐Julian Oscillation on Continental United States Hurricane Landfalls

ESD Ideas: Translating historical extreme weather events into a warmer world

Assessing homogeneity of land surface air temperature observations using sparse‐input reanalyses