A new coupled global NCEP Reanalysis for the period 1979-present is now available, at much higher temporal and spatial resolution, for climate studies. T he first reanalysis at NCEP (all acronyms are defined in the appendix), conducted in the 1990s, resulted in the NCEP-NCAR reanalysis (Kalnay et al. 1996), or R1 for brevity, and ultimately covered many years, from 1948 to the present (Kistler et al. 2001). It is still being executed at NCEP, to the benefit of countless users for monthly, and even daily, updates of the current state of the atmosphere. At the same time, other reanalyses were being conducted, namely, ERA-15 (Gibson et al. 1997) was executed for a more limited period (1979-93) at the ECMWF, COLA conducted a short reanalysis covering the May 1982-November 1983 period (Paolino et al. 1995), and NASA GSFC conducted a reanalysis covering the 1980-94 period (Schubert et al. 1997). The general purpose of conducting reanalyses is to produce multiyear global state-of-the-art gridded representations of atmospheric states, generated by a constant model and a constant data assimilation system. To use the same model and data assimilation over a very long period was the great advance during the 1990s, because gridded datasets available before 1995 had been created in real time by ever-changing models and analysis methods, even by hand analyses prior to about 1965. The hope was that a reanalysis,
A long-term, consistent, high-resolution climate dataset for the North American domain, as a major improvement upon the earlier global reanalysis datasets in both resolution and accuracy, is presented.
Arctic air masses have direct impacts on the weather and climatic extremes of midlatitude areas such as central North America. Arctic physical processes pose special and very important problems for global atmospheric models used for climate simulation and numerical weather prediction. At present, the observational database is inadequate to support research aimed at overcoming these problems. Three interdependent Arctic field programs now being planned will help to remedy this situation: SHEBA, which will operate an ice camp in the Arctic for a year; ARM, which will supply instruments for use at the SHEBA ice camp and which will also conduct longer-term measurements near Barrow, Alaska; and FIRE, which will conduct one or more aircraft campaigns, in conjunction with remote-sensing investigations focused on the SHEBA ice camp. This paper provides an introductory overview of the physics of the Arctic from the perspective of large-scale modelers, outlines some of the modeling problems that arise in attempting to simulate these processes, and explains how the data to be provided by the three field programs can be used to test and improve large-scale models.
There are a growing number of level 4 (L4; gap-free gridded) sea surface temperature (SST) products generated by blending SST data from various sources which are available for use in a wide variety of operational and scientific applications. In most cases, each product has been developed for a specific user community with specific requirements guiding the design of the product. Consequently Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site 2 differences between products are implicit. In addition, anomalous atmospheric conditions, satellite operations and production anomalies may occur which can introduce additional differences. This paper describes a new web-based system called the L4 SST Quality Monitor (L4-SQUAM) developed to monitor the quality of L4 SST products. L4-SQUAM intercompares thirteen L4 products with 1-day latency in an operational environment serving the needs of both L4 SST product users and producers. Relative differences between products are computed and visualized using maps, histograms, time series plots and Hovmöller diagrams, for all combinations of products. In addition, products are compared to quality controlled in situ SST data (available from the in situ SST Quality Monitor, iQUAM, companion system) in a consistent manner. A full history of products statistics is retained in L4-SQUAM for time series analysis. L4-SQUAM complements the two other Group for High Resolution SST (GHRSST) tools, the GHRSST Multi Product Ensemble (GMPE) and the High Resolution Diagnostic Data Set (HRDDS) systems, documented in part 1 of this paper and elsewhere, respectively. Our results reveal significant differences between SST products in coastal and open ocean areas. Differences of >2 °C are often observed at high latitudes partly due to different treatment of the sea-ice transition zone. Thus when an ice flag is available, the intercomparisons are performed in two ways: including and excluding ice-flagged grid points. Such differences are significant and call for a community effort to understand their root cause and ensure consistency between SST products. Future work focuses on including the remaining daily L4 SST products, accommodating for newer L4 SSTs which resolve the diurnal variability and evaluating retrospectively regenerated L4 SSTs to support satellite data reprocessing efforts aimed at generating improved SST Climate Data Records.
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