The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km.

Onboard both the Suomi National Polar-orbiting Partnership and Joint Polar Satellite System (JPSS) series of satellites, the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) is a new generation of a total ozone column sensor and is used to generate total column ozone products. This study presents a method for efficiently assessing OMPS-NM geolocation accuracy using spatially collocated radiance measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Band M1 by taking advantage of its high spatial resolution (750 m at nadir) and accurate geolocation. The basic idea is to find the best collocation position with maximum correlation between VIIRS collocated and real OMPS-NM radiances by perturbing OMPS-NM line-of-sight (LOS) vectors in the cross-track and along-track directions with small steps in the spacecraft coordinate. The perturbation angles at the best collocation position where OMPS-NM and VIIRS are optimally aligned are used to characterize OMPS-NM geolocation accuracy. In addition, the assessment results can be used to optimize the OMPS-NM field view angle lookup table in the Sensor Data Record (SDR) processing software to improve its geolocation accuracy. To demonstrate the methodology, the proposed method is successfully employed to evaluate OMPS-NM geolocation accuracy with different spatial resolutions. The results indicate that, after the view angle table was updated, the geolocation accuracy for both SNPP and NOAA-20 OMPS-NM is on the sub-pixel level (less than ¼ pixel size) along all the scan positions in both cross-track and along-track directions and the performance is very stable with time. The method proposed in this study lays down the framework for assessing the geolocation accuracy of future high-resolution OMPS-NM measurements.

The Nadir Mapper (NM) and Nadir Profiler (NP) within the Ozone Mapping and Profiler Suites (OMPS) are ultraviolet spectrometers to measure Earth radiance and Solar irradiance spectra from 300–380 nm and 250–310 nm, respectively. The OMPS NM and NP instruments flying on the Suomi-NPP (SNPP) satellite have provided over ten years of operational Sensor Data Records (SDRs) data sets to support a variety of OMPS Environmental Data Record (EDR) applications. However, the discrepancies of quality remain in the operational OMPS SDR data prior to 28 June 2021 due to changes in calibration algorithms associated with the calibration coefficient look-up tables (LUTs) during this period. In this study, we present results for the newly (v2) reprocessed SNPP OMPS NM and NP SDR data prior to 30 June 2021, which uses consistent calibration tables with improved accuracy. Compared with a previous (v1) reprocessing, this new reprocessing includes the improvements associated with the following updated tables or error correction: an updated stray light correction table for the NM, an off-nadir geolocation error correction for the NM, an artificial offset error correction in the NM dark processing code, and biweekly solar wavelength LUTs for the NP. This study further analyzes the impact of each improvement on the quality of the OMPS SDR data by taking advantage of the existing OMPS SDR calibration/validation studies. Finally, this study compares the v2 reprocessed OMPS data sets with the operational and the v1 reprocessed data sets. The results demonstrate that the new reprocessing significantly improves the accuracy and consistency of the life-time SNPP OMPS NM and NP SDR data sets. It also advances the uniformity of the data over the dichroic range from 300 to 310 nm between the NM and NP. The normalized radiance differences at the same wavelength between the NM and NP observations are reduced from 0.001 order (v1 reprocessing) or 0.01 order (operational processing) to 0.001 order or smaller. The v2 reprocessed data are archived in the NOAA CLASS data center with the same format as the operational data.

The NOAA-20 Ozone Mapping and Profiler Suite (OMPS) is the second OMPS flight unit in the US Joint Polar Satellite System (JPSS) program [1]. Flying on the NOAA-20 satellite, the OMPS extends the 40+-year total column ozone and ozone profile records to monitor ozone concentration in the Earth atmosphere. Recent studies have been conducted over a wide range of radiometric calibration and geolocation calibration areas to improve the quality of OMPS sensor data records. This study covers several topics, including the removal of a solar intrusion signal during science data collection, reduction of solar activity impact resulting from solar activities, update of geolocation accuracy via refinement of the instrument CCD spatial registration, and minimization of a misalignment at the edges of the sensor spatial field of view during Earth view measurements. The objective of this study is to improve OMPS instrument performance to OMPS users' expectations, and to advance the OMPS calibration accuracy of sensor data records above current product requirements [2], [3]. The solar intrusion correction improved local radiance retrieval accuracy 4% for the Nadir Profiler sensor, during science observations in the Northern hemisphere where solar zenith angle ranges from $60^{\circ}$ to $88^{\circ}$ . Solar model improvements achieve up to 1% better fidelity in irradiance measurements during solar observations. The calibrated CCD spatial registration minimizes the misalignment in the Nadir Mapper sensor spatial field of view in both cross track and along track measurements, and refines the geolocation accuracy to less than 5 km for the sensor's geolocation products that have a nominal spatial resolution of 50 km x 17 km.

This study carries out the calibration and validation of Antenna Temperature Data Record (TDR) and Brightness Temperature Sensor Data Record (SDR) data from the last National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-A (AMSU-A) flown on the Meteorological Operational satellite programme (MetOp)-C satellite. The calibration comprises the selection of optimal space view positions for the instrument and the determination of coefficients in calibration equations from the Raw Data Record (RDR) to TDR and SDR. The validation covers the analyses of the instrument noise equivalent differential temperature (NEDT) performance and the TDR and SDR data quality from the launch until 15 November 2019. In particular, the Metop-C data quality is assessed by comparing to radiative transfer model simulations and observations from Metop-A/B AMSU-A, respectively. The results demonstrate that the on-orbit instrument NEDTs have been stable since launch and continue to meet the specifications at most channels except for channel 3, whose NEDT exceeds the specification after April 2019. The quality of the Metop-C AMSU-A data for all channels except channel 3 have been reliable since launch. The quality at channel 3 is degraded due to the noise exceeding the specification. Compared to its TDR data, the Metop-C AMSU-A SDR data exhibit a reduced and more symmetric scan angle-dependent bias against radiative transfer model simulations, demonstrating the great performance of the TDR to SDR conversion coefficients. Additionally, the Metop-C AMSU-A data quality agrees well with Metop-A/B AMSU-A data, with an averaged difference in the order of 0.3 K, which is confirmed based on Simultaneous Nadir Overpass (SNO) inter-sensor comparisons between Metop-A/B/C AMSU-A instruments via either NOAA-18 or NOAA-19 AMSU-A as a transfer.

The performance of on-orbit satellite microwave instrument noise equivalent differential temperature (NEDT) and Temperature Data Record (TDR) data is always critical for the broad user community in both environmental data record (EDR) product retrieval and numerical weather prediction applications. Based on intensive pre-launch and post-launch calibration studies [1]-[5], this study assesses the post-launch performance of Metop-C AMSU-A instrument noise and antenna temperature data since the launch. It also conducts the quality assessment of the AMSU-A data in comparison with radiative transfer model (RTM) simulations, legacy NOAA-18/19 and Metop-A/B AMSU-A observations, and NOAA-20 ATMS observations. Finally, this study addresses impact of polarization and wavelength inconsistency on AMSU-A antenna temperature differences against ATMS.

An updated version of the microwave-integrated retrieval system (MiRS) V11.2 was recently released. In addition to the previous capability to process multiple satellites/sensors, the new version has been extended to process global precipitation measurement (GPM) microwave imager (GMI) measurements. The main purpose of this study is to introduce MiRS GPM products and to evaluate rain rate, total precipitable water (TPW), and snow water equivalent (SWE) using various independent datasets. Rain rate evaluations were performed for January, April, July, and October 2015 which represents one full month in each season. TPW was evaluated on four days: 9 January, 1 April, 13 July, and 1 October, which represents one full day in each season. SWE was evaluated for a week in January 2015. Results show that MiRS performance is generally satisfactory in regards to both global/regional geographical distribution and quantified statistical/categorical scores. Histograms show that MiRS GPM rain rate estimates have the capability to reproduce moderate to heavy rain frequency distribution over land, and light rain distribution over ocean when compared with a ground-based reference. Evaluations of TPW show the best performance over ocean with the correlation coefficient, bias, and standard deviation of 0.99, <1.25 mm, and <2.4 mm, respectively. Robust statistical results were also obtained for SWE, with a correlation coefficient, bias, and standard deviation of 0.77, 1.72 cm, and 3.61 cm, respectively. The examples shown demonstrate that MiRS, now extended to GPM/GMI, is capable of producing realistic retrieval products that can be used in broad applications including extreme weather events monitoring, depiction of global rainfall distribution, and water vapor patterns, as well as snow cover monitoring.

The NOAA Microwave Integrated Retrieval System (MiRS) retrieves the atmospheric profiles of state variables (temperature, moisture, liquid cloud, hydrometeors, surface emissivity spectrum and skin temperature) simultaneously and coherently with one-dimension Variational (1DVar) method based on passive microwave radiance observation from multiple polar-orbiting operational satellites, including NOAA18, NOAA19, MetOp (A and B), DMSP (F16 and F18) and SNPP. In MiRS, the geophysical consistency between the retrieved state variables is obtained through the constraint from the state variable background covariance matrices, which define the variability of each state variable and the relationship between them. The current atmospheric covariance matrices were mainly built based on the ECMWF 60 layer sample data set (EC60). Because no rain data available in EC60, the MM5 model output rain was used as a substitute. Although MiRS performs well based on current matrices, obviously, there is room for improvement if the matrices could be rebuilt based more representative, higher resolution dataset, and most importantly, all variables are from one datasets, so the cross relationship between parameters could be properly represented. The ECMWF IFS-137 dataset (EC137) is the most recent ECMWF sample data set. Besides several major modifications and improvements in ECMWF forecasting system, comparing with EC60, the profiles in EC137 have more than doubled vertical resolution. The hydrometeor variables are included in EC137, rather than in EC60. With upgraded sampling method, the profiles in EC137 are more evenly distributed in both temporal and spatial domains. And the profile population in EC137 shows significant different statistic characters than its precedents. To generate the new matrices, EC137 data is interpolated from 137 layers to 100 layers. The variable units are transferred to be used for radiation transfer calculation and match with those used in MiRS. For example, the unit of rain and snow is transferred from mass flux (Kg/(m2*s)) to water content (Kg/m2) with the assumption of rain (snow) drop speed as 4.0 (1.0) m/s. Comparing the matrix based on EC137 and that of current MiRS, except the variation in details, there are two significant distinct features are noticeable. First, the snow variable becomes effective in EC137 matrix because the snow variable is provided in EC137 rather than in EC60 and MM5. Second, the covariance between rain and other variables becomes meaningful in EC137 matrix. In the old matrix, rain is not well correlated with other variables, as rain data is from MM5, but other parameters are from EC60, two unrelated datasets. All these differences imply different, most likely better performance of MiRS system if employing the background covariance matrices based on EC137. Tuning is conducted after implementation of the new EC137 matrices. And the impact of the new matrices is assessed by comparing the retrieval products based on the new and old matrices, and with colloated dropsonde, radiosonde and ECMWF analysis profiles.

The 4th WCRP International Conference on Reanalyses provided an opportunity for the international community to review and discuss the observational and modelling research, as well as process studies and uncertainties associated with reanalysis of the Earth System and its components. Characterizing the uncertainty and quality of reanalyses is a task that reaches far beyond the international community of producers, and into the interdisciplinary research community, especially those using reanalysis products in their research and applications. Reanalyses have progressed greatly even in the last 5 years, and newer ideas, projects and data are coming forward. While reanalysis has typically been carried out for the individual domains of atmosphere, ocean and land, it is now moving towards coupling using Earth system models. Observations are being reprocessed and they are providing improved quality for use in reanalysis. New applications are being investigated, and the need for climate reanalyses is as strong as ever. At the heart of it all, new investigators are exploring the possibilities for reanalysis, and developing new ideas in research and applications. Given the many centres creating reanalyses products (e.g. ocean, land and cryosphere research centres as well as NWP and atmospheric centers), and the development of new ideas (e.g. families of reanalyses), the total number of reanalyses is increasing greatly, with new and innovative diagnostics and output data. The need for reanalysis data is growing steadily, and likewise, the need for open discussion and comment on the data. The 4th Conference was convened to provide a forum for constructive discussion on the objectives, strengths and weaknesses of reanalyses, indicating potential development paths for the future.

AbstractLike all reanalysis efforts, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) must contend with an inhomogeneous observing network. Here the effects of the two most obvious observing system epoch changes, the Advanced Microwave Sounding Unit-A (AMSU-A) series in late 1998 and, to a lesser extent, the earlier advent of the Special Sensor Microwave Imager (SSM/I) in late 1987 are examined. These sensor changes affect model moisture and enthalpy increments and thus water and energy fluxes, since the latter result from model physics processes that respond sensitively to state variable forcing. Inclusion of the analysis increments in the MERRA dataset is a unique feature among reanalyses that facilitates understanding the relationships between analysis forcing and flux response.In stepwise fashion in time, the vertically integrated global-mean moisture increments change sign from drying to moistening and heating increments drop nearly 15 W m−2 over the 30 plus years of the as...

Regional water cycles of reanalyses do not follow theoretical assumptions applicable to pure simulated budgets. The data analysis changes the wind, temperature and moisture, perturbing the theoretical balance. Of course, the analysis is correcting the model forecast error, so that the state fields should be more aligned with observations. Recently, it has been reported that the moisture convergence over continental regions, even those with significant quantities of radiosonde profiles present, can produce long term values not consistent with theoretical bounds. Specifically, long averages over continents produce some regions of moisture divergence. This implies that the observational analysis leads to a source of water in the region. One such region is the Unite States Great Plains, which many radiosonde and lidar wind observations are assimilated. We will utilize a new ancillary data set from the MERRA reanalysis called the Gridded Innovations and Observations (GIO) which provides the assimilated observations on MERRA's native grid allowing more thorough consideration of their impact on regional and global climatology. Included with the GIO data are the observation minus forecast (OmF) and observation minus analysis (OmA). Using OmF and OmA, we can identify the bias of the analysis against each observing system and gain a better understanding of the observations that are controlling the regional analysis. In this study we will focus on the wind and moisture assimilation.

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) was undertaken by NASA's Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA's Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses. Focusing on the satellite era, from 1979 to the present, MERRA has achieved its goals with significant improvements in precipitation and water vapor climatology. Here, a brief overview of the system and some aspects of its performance, including quality assessment diagnostics from innovation and residual statistics, is given.By comparing MERRA with other updated reanalyses [the interim version of the next ECMWF ReAnalysis (ERA-Interim) and the Climate Forecast System Reanalysis (CFSR)], advances made in this new generation of reanalyses, as well as remaining deficiencies, are identified. Although there is little difference between the new reanalyses in many aspects of climate variability, substantial differences remain in poorly constrained quantities such as precipitation and surface fluxes. These differences, due to variations both in the models and in the analysis techniques, are an important measure of the uncertainty in reanalysis products. It is also found that all reanalyses are still quite sensitive to observing system changes. Dealing with this sensitivity remains the most pressing challenge for the next generation of reanalyses.Production has now caught up to the current period and MERRA is being continued as a near-real-time climate analysis. The output is available online through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC).

Atmospheric states from the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the North American Regional Reanalysis (NARR) are compared with data from the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site, including the ARM continuous forcing product and Cloud Modeling Best Estimate (CMBE) soundings, during the period 1999–2001 to understand their validity for single-column model (SCM) and cloud-resolving model (CRM) forcing datasets. Cloud fraction, precipitation, and radiation information are also compared to determine what errors exist within these reanalyses. For the atmospheric state, ARM continuous forcing and the reanalyses have good agreement with the CMBE sounding information, with biases generally within 0.5 K for temperature, 0.5 m s−1 for wind, and 5% for relative humidity. Larger disagreements occur in the upper troposphere (p < 300 hPa) for temperature, humidity, and zonal wind, and in the boundary layer (p > 800 hPa) for meridional wind and humidity. In these regions, larger errors may exist in derived forcing products. Significant differences exist for vertical pressure velocity, with the largest biases occurring during the spring upwelling and summer downwelling periods. Although NARR and MERRA share many resemblances to each other, ARM outperforms these reanalyses in terms of correlation with cloud fraction. Because the ARM forcing is constrained by observed precipitation that gives the adequate mass, heat, and moisture budgets, much of the precipitation (specifically during the late spring/early summer) is caused by smaller-scale forcing that is not captured by the reanalyses. While reanalysis-based forcing appears to be feasible for the majority of the year at this location, it may have limited usage during the late spring and early summer, when convection is common at the ARM SGP site. Both NARR and MERRA capture the seasonal variation of cloud fractions (CFs) observed by ARM radar–lidar and Geostationary Operational Environmental Satellite (GOES) with high correlations (0.92–0.78) but with negative biases of 14% and 3%, respectively. Compared to the ARM observations, MERRA shows better agreement for both shortwave (SW) and longwave (LW) fluxes except for LW-down (due to a negative bias in water vapor): NARR has significant positive bias for SW-down and negative bias for LW-down under clear-sky and all-sky conditions. The NARR biases result from a combination of too few clouds and a lack of sufficient extinction by aerosols and water vapor in the atmospheric column. The results presented here represent only one location for a limited period, and more comparisons at different locations and longer periods are needed.

Reanalyses, retrospectively analyzing observations over climatological time scales, represent a merger between satellite observations and models to provide globally continuous data and have improved over several generations. Balancing the earth's global water and energy budgets has been a focus of research for more than two decades. Models tend to their own climate while remotely sensed observations have had varying degrees of uncertainty. This study evaluates the latest NASA reanalysis, the Modern Era Retrospective-Analysis for Research and Applications (MERRA), from a global water and energy cycles perspective, to place it in context of previous work and demonstrate the strengths and weaknesses.MERRA was configured to provide complete budgets in its output diagnostics, including the incremental analysis update (IAU), the term that represents the observations influence on the analyzed states, alongside the physical flux terms. Precipitation in reanalyses is typically sensitive to the observational analysis. For MERRA, the global mean precipitation bias and spatial variability are more comparable to merged satellite observations [the Global Precipitation and Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP)] than previous generations of reanalyses. MERRA ocean evaporation also has a much lower value, which is comparable to independently derived estimate datasets. The global energy budget shows that MERRA cloud effects may be generally weak, leading to excess shortwave radiation reaching the ocean surface.Evaluating the MERRA time series of budget terms, a significant change occurs that does not appear to be represented in observations. In 1999, the global analysis increments of water vapor changes sign from negative to positive and primarily lead to more oceanic precipitation. This change is coincident with the beginning of Advanced Microwave Sounding Unit (AMSU) radiance assimilation. Previous and current reanalyses all exhibit some sensitivity to perturbations in the observation record, and this remains a significant research topic for reanalysis development. The effect of the changing observing system is evaluated for MERRA water and energy budget terms.

In this study, the atmospheric state, precipitation, cloud fraction, and radiative fluxes from Modern Era Retrospective-analysis for Research and Applications (MERRA) and North American Regional Reanalysis (NARR) are collected and compared with the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) continuous forcing during the period 1999-2001. For the atmospheric state, the three datasets have excellent agreement for the horizontal wind components and air temperature. NARR and ARM have generally good agreement for humidity, except for several biases in the PBL and in the upper troposphere. MERRA, on the other hand, suffers from a year-round negative bias in humidity except for the month of June. For the vertical pressure velocity, significant differences exist with the largest biases occurring during the spring upwelling and summer downwelling periods. Although NARR and MERRA share many resemblances to each other, ARM outperforms these reanalyses in terms of correlation with cloud fraction. Because the ARM forcing is constrained by observed precipitation that gives the adequate mass, heat, and moisture budgets, much of the precipitation (specifically during the late spring/early summer) is caused by smaller-scale forcing that is not captured by the reanalyses. Both NARR and MERRA capture the seasonal variation of CF observed by ARM radar-lidar and GOES with high correlations (0.92-0.78), but having negative biases of 14% and 3%, respectively. Compared to the ARM observations, MERRA shows a better agreement for both SW and LW fluxes except for LW-down (due to a negative bias in water vapor), NARR has significant positive bias for SW-down and negative bias for LW-down under clear- and all-sky conditions . The NARR biases result from a combination of too few clouds and a lack of sufficient extinction by aerosols and water vapor in the atmospheric column. The results presented here represent only one location for a limited time period, and more comparisons at different locations and longer time period are needed.

The observed climate trends over the United States during 1950-2000 exhibit distinct seasonality and regionality. The surface air temperature exhibits a warming trend during winter, spring, and early summer and a modest countrywide cooling trend in late summer and fall, with the strongest warming occurring over the northern United States in spring. Precipitation trends are positive in all seasons, with the largest trend occurring over the central and southern United States in fall. This study investigates the causes of the seasonality and regionality of those trends, with a focus on the cooling and wetting trends in the central United States during late summer and fall. In particular, the authors examine the link between the seasonality and regionality of the climate trends over the United States and the leading patterns of sea surface temperature (SST) variability, including a global warming (GW) pattern and a Pacific decadal variability (PDV) pattern.A series of idealized atmospheric general circulation model (AGCM) experiments were performed forced by SST trends associated with these leading SST patterns, as well as the residual trend pattern ( obtained by removing the GW and PDV contributions). The results show that the observed seasonal and spatial variations of the climate trends over the United States are to a large extent explained by changes in SST. Among the leading patterns of SST variability, the PDV pattern plays a prominent role in producing both the seasonality and regionality of the climate trends over the United States. In particular, it is the main contributor to the apparent cooling and wetting trends over the central United States. The residual SST trend, a manifestation of phase changes of the Atlantic multidecadal SST variation during 1950-2000, also exerts influences that show strong seasonality with important contributions to the central U. S. temperature and precipitation during the summer and fall seasons. In contrast, the response over the United States to the GW SST pattern is an overall warming with little seasonality or regional variation. These results highlight the important contributions of decadal and multidecadal variability in the Pacific and Atlantic in explaining the observed seasonality and regionality of the climate trends over the United States during the period of 1950-2000.

The spatiotemporal structure of Pacific pan-decadal variability (PDV) is isolated in global long-term surface temperature (ST) datasets and reanalysis atmospheric parameter fields from which El Nino Southern Oscillation (ENSO) effects have been removed. Empirical orthogonal function (EOF) and combined EOF analysis of the resulting time series identify PDV as one of two primary modes of long-term variability, the other being a global warming (GW) trend, which is addressed in a companion paper (Part I). In this study, it is shown that one of several PDV interdecadal regime shifts occurred during the 1990s. This significant change in the Pacific basin is comparable but antiphase to the well-known 1976 climate regime shift and is consistent with the observed changes in biosystems and ocean circulation. A comprehensive picture of PDV as manifested in the troposphere and at the surface is described. In general, the PDV spatial patterns in different parameter fields share some similarities with the patterns associated with ENSO, but important differences exist. First, the PDV circulation pattern is shifted westward by about 20 and is less zonally extended than that for ENSO. The westward shift of the PDV wave train produces a different North American teleconnection pattern that is more west-east oriented. The lack of a strong PDV surface temperature (ST) signal in the west equatorial Pacific and the relatively strong ST signal in the subtropical regions are consistent with an atmospheric overturning circulation response that differs from the one associated with ENSO. The analysis also suggests that PDV is a combination of decadal and/or interdecadal oscillations interacting through teleconnections.