YODEN Shigeo Dept. of Geophysics, Kyoto Univ., JAPAN Highlights for SPARC Temperature Trend Meeting at Tabard Inn in Washington DC April 12-13, 2007 Seasonally.

Slides:

Advertisements

Similar presentations

Long-Term Stratospheric Temperature Time Series for SPARC Temperature Trends Assessment Paper Dian Seidel April 2007 Tabard Inn, Washington DC.

Advertisements

C O M P U T A T I O N A L R E S E A R C H D I V I S I O N You need how many runs?! Michael F. Wehner Lawrence Berkeley National Laboratory

Forecasting OPS 370.

Forecasting Using the Simple Linear Regression Model and Correlation

Februar 2003 Workshop Kopenhagen1 Assessing the uncertainties in regional climate predictions of the 20 th and 21 th century Andreas Hense Meteorologisches.

A Possible Impact Way of the Stratosphere on Troposphere LI Chongyin, PAN Jing LASG, Institute of Atmospheric Physics ASM-STE Lhasa, July 21-23, 2010.

COMPARISON OF AIR TEMPERATURE TRENDS BASED ON REANALYSIS DATA, MODEL SIMULATIONS DATA AND AEROLOGICAL OBSERVATIONS V.M. Khan, K.G. Rubinshtain, Hydrometeorological.

YODEN Shigeo Dept. of Geophysics, Kyoto Univ., JAPAN August 4, 2004; SPARC 2004 Victoria ＋ α － β for Colloquium on April 15, Introduction 2.Internal.

21/11/20071 Global Ozone: Past and Present Abhinand Jha PEP Universität Bremen Presented for Presentation Tech. Course.

3. Climate Change 3.1 Observations 3.2 Theory of Climate Change 3.3 Climate Change Prediction 3.4 The IPCC Process.

Large-scale atmospheric circulation characteristics and their relations to local daily precipitation extremes in Hesse, central Germany Anahita Amiri Department.

Global Climate Change's Effects On Agriculture

Longer-Term Trends Dian Seidel SPARC Temperature Trends Panel Meeting July 2006 Abingdon.

SIMPLE LINEAR REGRESSION

YODEN Shigeo Dept. of Geophysics, Kyoto Univ., JAPAN March 3-4, 2005: SPARC Temperature Trend Meeting at University of Reading 1.Introduction 2.Statistical.

Chapter Topics Types of Regression Models

INTERDECADAL OSCILLATIONS OF THE SOUTH AMERICAN MONSOON AND THEIR RELATIONSHIP WITH SEA SURFACE TEMPERATURE João Paulo Jankowski Saboia Alice Marlene Grimm.

National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory Princeton, NJ Evolution of Stratospheric.

Attribution of Stratospheric Temperature Trends to Forcings A coupled chemistry-climate model (CCM) study Richard S. Stolarski NASA GSFC In collaboration.

Seasonal outlook of the East Asian Summer in 2015 Motoaki Takekawa Tokyo Climate Center Japan Meteorological Agency May th FOCRAII 1.

SIMPLE LINEAR REGRESSION

Climate Graphs.

Statistical Methods For Engineers ChE 477 (UO Lab) Larry Baxter & Stan Harding Brigham Young University.

Introduction to Linear Regression and Correlation Analysis

The case of polar lows Hans von Storch 13 and Matthias Zahn 2 1. Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Germany. 2. Environmental.

Linear Trend Lines Y t = b 0 + b 1 X t Where Y t is the dependent variable being forecasted X t is the independent variable being used to explain Y. In.

April 24, 2007 Nihat Cubukcu Utilization of Numerical Weather Forecast in Energy Sector.

1. Introduction 3. Global-Scale Results 2. Methods and Data Early spring SWE for historic ( ) and future ( ) periods were simulated. Early.

Figure 1. Primary explanatory variables (basis functions) considered in the multiple regression statistical model. See the text for data sources.  Log.

Regional climate prediction comparisons via statistical upscaling and downscaling Peter Guttorp University of Washington Norwegian Computing Center

TRENDS IN ATMOSPHERIC OZONE FROM A LONG-TERM OZONE CLIMATOLOGY Jane Liu 1,2, D. W. Tarasick 3, V. E. Fioletov 3, C. McLinden 3, J. H. Y. Jung 1, T. Zhao.

Seasonal Climate Cycles Solar Radiation at the Earth’s Surface.

ANALISIS OF OBSERVED GLOBAL AND REGIONAL CLIMATE CHANGE Konstantin Vinnikov Department Atmospheric and Oceanic Science College of Computer, Mathematical.

C20C Workshop, ICTP Trieste 2004 The impact of stratospheric ozone depletion and CO 2 on tropical cyclone behaviour in the Australian region Syktus J.

Recent variability of the solar spectral irradiance and its impact on climate modelling - TOSCA WG1 Workshop, May 2012, Berlin Stratospheric and tropospheric.

Shigeo Yoden Department of Geophysics Kyoto University Japan ESF-JSPS Frontier Science Conference Series for Young Researchers « Climate Change » Nynäshamn.

Objective Data  The outlined square marks the area of the study arranged in most cases in a coarse 24X24 grid.  Data from the NASA Langley Research Center.

A Statistical Analysis on the Stratosphere-Troposphere Coupled Variability by Using Large Samples obtained from a Mechanistic Circulation Model Yoko NAITO.

The State of the Atmosphere 1.Atmospheric mass and pressure 2.Temperature structure 3.Geopotential 4.Circulation 5.Water in the atmosphere.

3. Products of the EPS for three-month outlook 1) Outline of the EPS 2) Examples of products 3) Performance of the system.

Seasonal Variation and Test of Qinghai-Tibetan Plateau Heating and Its Profile Zhong Shanshan, He Jinhai Key Laboratory of Meteorological Disasters of.

2.There are two fundamentally different approaches to this problem. One can try to fit a theoretical distribution, such as a GEV or a GP distribution,

Infrasounds and Background Free Oscillations Naoki Kobayashi [1] T. Kusumi and N. Suda [2] [1] Tokyo Tech [2] Hiroshima Univ.

Scatter Diagrams scatter plot scatter diagram A scatter plot is a graph that may be used to represent the relationship between two variables. Also referred.

28. Multiple regression The Practice of Statistics in the Life Sciences Second Edition.

RADIOSONDE TEMPERATURE BIAS ESTIMATION USING A VARIATIONAL APPROACH Marco Milan Vienna 19/04/2012.

Global Climate Change: Detection and Attribution Finish Idealized Global Circulation Model and Global Climates Is the Current Climate Changing? If so,

Tendencies of change for different characteristics of extratropical cyclones in the Northern Hemisphere Mirseid G. Akperov and Igor I. Mokhov A. M. Obukhov.

Mukougawa, Hitoshi 1, Yuhji Kuroda 2 and Toshihiko Hirooka 3 1 Disaster Prevention Research Institute, Kyoto University, JAPAN 2 Meteorological Research.

Observed Recent Changes in the Tropopause Dian Seidel NOAA Air Resources Laboratory ~ Silver Spring, Maryland USA Bill Randel NCAR Atmospheric Chemistry.

A SEARCH FOR FREE ROSSBY WAVES AND FOR VAN LOON’S TEMPERATURE VARIATIONS IN THE SOUTHERN HEMISPHERE STRATOSPHERE Roland Madden, NCAR, USA.

1 Detection of discontinuities using an approach based on regression models and application to benchmark temperature by Lucie Vincent Climate Research.

Evaluation of Various Datasets for Tropical Storm Climatology Asuka Suzuki.

Chapter 4 Minitab Recipe Cards. Correlation coefficients Enter the data from Example 4.1 in columns C1 and C2 of the worksheet.

“CLIMATE IS WHAT WE EXPECT, AND WEATHER IS WHAT WE GET” ~ MARK TWAIN.

UBC/UW 2011 Hydrology and Water Resources Symposium Friday, September 30, 2011 DIAGNOSIS OF CHANGING COOL SEASON PRECIPITATION STATISTICS IN THE WESTERN.

 Each climate graph is made up of 2 major parts. ◦ A line graph for temperature.  Represented by a red line for the high temperatures  Represented.

THE INFLUENCE OF THE 11-YEAR SOLAR CYCLE ON THE STRATOSPHERE BELOW 30KM: A REVIEW H. VAN LOON K. LABITZKE 2010/04/13 Pei-Yu Chueh.

MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.

Dynamical control of ozone transport and chemistry from satellite observations and coupled chemistry climate models Mark Weber 1, Sandip Dhomse 1, Ingo.

NOAA Northeast Regional Climate Center Dr. Lee Tryhorn NOAA Climate Literacy Workshop April 2010 NOAA Northeast Regional Climate.

Intraseasonal, seasonal, and interannual variations of the Arctic temperature in paleoclimates, present, and future experiments in CMIP5 model outputs.

1Weidinger, T., 2Costa, A. A., 3Lajos, T., 4Kiss, Á.,

El Nino and La Nina An important atmospheric variation that has an average period of three to seven years. Goes between El Nino, Neutral, and La Nina (ENSO.

SOLARIS activity report

Therefore, the Age variable is a categorical variable.

Understanding and constraining snow albedo feedback

PROVIDING THE UPPER-AIR DATA RELEVANT TO STUDIES OF THE NORTHERN POLAR CLIMATE CHANGES Alexander M. Sterin (Russian Research Institute for Hydrometeorological.

Hydrology Modeling in Alaska: Modeling Overview

Wenche Aas, Hilde Fagerli, Svetlana Tsyro, Sverre Solberg

Presentation transcript:

YODEN Shigeo Dept. of Geophysics, Kyoto Univ., JAPAN Highlights for SPARC Temperature Trend Meeting at Tabard Inn in Washington DC April 12-13, 2007 Seasonally Dependent Detectability of a Linear Trend Submitted to JGR

 detectability of a linear trend in a finite-length dataset natural internal variability causes a spurious trend it may have a non-Gaussian nature  we argue the minimum length of dataset necessary for the detection of a given magnitude of trend with a given statistical significance level the minimum magnitude of trend for a given length of dataset with a given statistical significance level  Edgeworth expansion of the distribution function of the spurious trend estimation of higher moments by long time integrations of numerical models under a purely periodic annual forcing  two examples stratospheric polar temperature (15,200-year dataset) precipitation (1,000-year dataset)

 Labitzke diagram seasonal variation of histograms of monthly mean T (30hPa) North Pole (Berlin) North Pole (NCEP) Nishizawa and Yoden(2005) An MCM result observational data are too short model output could be used to estimate the moments

 the minimum magnitude of trend for a 20-year dataset with the confidence coefficient of 90% < 0.5 K/decade > 5 K/decade

 statistical significance of the three trends estimated with the moments of our numerical model output JAN JUL ERA40 NCEP Berlin 99% 95% 90% 95% 99%

Concluding remarks  The detectability depends on distribution function of the spurious trend due to the limited length of dataset with natural internal variability. Gaussian: Student’s t distribution of the spurious trend Non-Gaussian: use of the Edgeworth expansion  We can estimate the distribution function with the moments of the internal variability obtained by long time integrations of atmospheric numerical models.  the minimum length of dataset necessary for the detection of a given magnitude of trend  the minimum magnitude of trend for a given length of dataset

 Two typical examples that have large seasonal and regional dependence of natural internal variability: stratospheric polar temperature in the Northern Hemisphere precipitation in the equatorial region  The detectability has large dependence on season and region. it is not always appropriate to use annual or global average careful consideration is necessary to choose season and region as well as quantity for detection of a statistically significant linear trend with a given length of dataset