The western Colombia low-level jet and its simulation by CMIP5 models

Similar documents
Research Article Precipitation over Northern South America and Its Seasonal Variability as Simulated by the CMIP5 Models

Instituto Geofisico del Perú (IGP), Lima, Peru; 2. University at Albany- State University of New York, New York, USA; 3

WATER VAPOR FLUXES OVER EQUATORIAL CENTRAL AFRICA

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP July 26, 2004

High-Resolution MPAS Simulations for Analysis of Climate Change Effects on Weather Extremes

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 11 November 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 5 August 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 25 February 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 15 July 2013

Predictability and prediction of the North Atlantic Oscillation

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012

General Circulation. Nili Harnik DEES, Lamont-Doherty Earth Observatory

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 24 September 2012

Characteristics of extreme convection over equatorial America and Africa

GPC Exeter forecast for winter Crown copyright Met Office

El Niño, South American Monsoon, and Atlantic Niño links as detected by a. TOPEX/Jason Observations

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 9 November 2015

Impact of Eurasian spring snow decrement on East Asian summer precipitation

The Australian Summer Monsoon

Mozambique. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1

the 2 past three decades

Role of the Caribbean low-level jet and the Choco jet in the patterns of atmospheric moisture transport towards Central America

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 30 October 2017

30th Conference on Hurricanes and Tropical Meteorology, April 2012, Ponte Vedra Beach, Florida

Characteristics of Global Precipitable Water Revealed by COSMIC Measurements

IAP Dynamical Seasonal Prediction System and its applications

Understanding the regional pattern of projected future changes in extreme precipitation

Climate Forecast Applications Network (CFAN)

Malawi. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1

Antarctic sea-ice expansion between 2000 and 2014 driven by tropical Pacific decadal climate variability

The Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times

Sensitivity of zonal-mean circulation to air-sea roughness in climate models

Development of a Coupled Atmosphere-Ocean-Land General Circulation Model (GCM) at the Frontier Research Center for Global Change

Pacific Storm Track at Different Horizontal Resolutions Snap-shot of Column Liquid Water Content

Verification of the Seasonal Forecast for the 2005/06 Winter

BMKG Research on Air sea interaction modeling for YMC

Climate Outlook for March August 2017

PRMS WHITE PAPER 2014 NORTH ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Event Response

Correspondence between short and long timescale systematic errors in CAM4/CAM5 explored by YOTC data

Evaluation of the IPSL climate model in a weather-forecast mode

New Zealand Climate Update No 223, January 2018 Current climate December 2017

Winter Steve Todd Meteorologist In Charge National Weather Service Portland, OR

Introduction of products for Climate System Monitoring

Introduction of climate monitoring and analysis products for one-month forecast

SUPPLEMENTARY INFORMATION

ENSO Outlook by JMA. Hiroyuki Sugimoto. El Niño Monitoring and Prediction Group Climate Prediction Division Japan Meteorological Agency

Temperature and rainfall changes over East Africa from multi-gcm forced RegCM projections

INFLUENCE OF LARGE-SCALE ATMOSPHERIC MOISTURE FLUXES ON THE INTERANNUAL TO MULTIDECADAL RAINFALL VARIABILITY OF THE WEST AFRICAN MONSOON

Characteristics of Storm Tracks in JMA s Seasonal Forecast Model

CLIMATE SIMULATION AND ASSESSMENT OF PREDICTABILITY OF RAINFALL IN THE SOUTHEASTERN SOUTH AMERICA REGION USING THE CPTEC/COLA ATMOSPHERIC MODEL

Different impacts of Northern, Tropical and Southern volcanic eruptions on the tropical Pacific SST in the last millennium

Vertical Moist Thermodynamic Structure of the MJO in AIRS Observations: An Update and A Comparison to ECMWF Interim Reanalysis

Wind: Global Systems Chapter 10

NOAA 2015 Updated Atlantic Hurricane Season Outlook

KUALA LUMPUR MONSOON ACTIVITY CENT

SUPPLEMENTARY INFORMATION

The ECMWF prototype for coupled reanalysis. Patrick Laloyaux

Introduction to Climate ~ Part I ~

El Niño 2015/2016 Impact Analysis Monthly Outlook February 2016

Zambia. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. C. McSweeney 1, M. New 1,2 and G.

No. 20 Spring El Niño Outlook (April October 2010) 1. Contents. (a) (a) (b) (b) Tokyo Climate Center 1 No. 20 Spring 2010

Trends in Climate Teleconnections and Effects on the Midwest

The ENSO s Effect on Eastern China Rainfall in the Following Early Summer

Climate Outlook for March August 2018

Rainfall Patterns across Puerto Rico: The Rate of Change

New Zealand Climate Update No 221, October 2017 Current climate October 2017

lecture 10 El Niño and the Southern Oscillation (ENSO) Part I sea surface height anomalies as measured by satellite altimetry

St Lucia. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. Precipitation

Recent Results with the GFDL High- Resolution Coupled Modeling Systems

The 2009 Hurricane Season Overview

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017)

Trends in the Character of Hurricanes and their Impact on Heavy Rainfall across the Carolinas

ENSO and April SAT in MSA. This link is critical for our regression analysis where ENSO and

Sierra Weather and Climate Update

California 120 Day Precipitation Outlook Issued Tom Dunklee Global Climate Center

Seasonal forecasting activities at ECMWF

Seasonal Climate Outlook for South Asia (June to September) Issued in May 2014

How Volcanism Impacts on Tropical Atlantic PPT. Luciana F. Prado Ilana Wainer

Grenada. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. Precipitation

Suriname. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. C. McSweeney 1, M. New 1,2 and G.

2013 ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Cat Response

Impacts of modes of climate variability, monsoons, ENSO, annular modes

Connecting tropics and extra-tropics: interaction of physical and dynamical processes in atmospheric teleconnections

Introduction of Seasonal Forecast Guidance. TCC Training Seminar on Seasonal Prediction Products November 2013

Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble

Will a warmer world change Queensland s rainfall?

THE INFLUENCE OF CLIMATE TELECONNECTIONS ON WINTER TEMPERATURES IN WESTERN NEW YORK INTRODUCTION

7 December 2016 Tokyo Climate Center, Japan Meteorological Agency

Linkages between Arctic sea ice loss and midlatitude

The Planetary Circulation System

Components of precipitation and temperature anomalies and change associated with modes of the Southern Hemisphere

Satellites, Weather and Climate Module??: Polar Vortex

Conference on Teleconnections in the Atmosphere and Oceans November Fall-to-winter changes in the El Nino teleconnection

The feature of atmospheric circulation in the extremely warm winter 2006/2007

North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Last updated: August 2009

1. INTRODUCTION 2. HIGHLIGHTS

Lecture 8. Monsoons and the seasonal variation of tropical circulation and rainfall

REGIONAL SIMULATION WITH THE PRECIS MODEL

Seasonal Climate Watch July to November 2018

Theoretical and Modeling Issues Related to ISO/MJO

Transcription:

The western Colombia low-level jet and its simulation by CMIP5 models Juan P. Sierra, Jhoana Agudelo, Paola A. Arias and Sara C. Vieira Grupo de Ingeniería y Gestión Amiental (GIGA), Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia

1. Motivation Importance of Choco low-level jet Choco jet transports important quantities of moisture from the Pacific ocean into western Colombia (Poveda and Mesa, 1999), especially during SON (Sakamoto et al. 2011; Arias et al. 2015). The interaction of this jet with the ITCZ, the CLLJ, and the EMEJ, induces the formation of MCSs (Sakamoto et al. 2011). The MCSs contribute to 70% of annual precipitation over the western part of Colombia (Zuluaga and Poveda, 2004). Choco low-level jet and climate change The Choco jet was stronger during the last glaciation due to an increased SST gradient, turning western Colombia wetter for this period (Martínez et al. 2003). What could happen with the Choco jet under a warmer climate? The first step is the representation of the present time features of this jet.

2. Datasets ERA-Interim reanalysis (Dee et al. 2011). Global Precipitation Climatology Project (GPCP) (Adler et al. 2003). TRMM rainfall data 3B43 V7 (Huffman et al. 2007). ETOPO1 data from NOAA (Amante and Eakins, 2009). 26 coupled and 11 uncoupled Models from the CMIP5 project. Global Climate

3. Methods GCM historical experiment of the CMIP5 runs. Time domain: 1979-2005. Use of AMIP and CMIP models. Original models resolution and bilinear interpolation for ensemble means (2.8 x2.8 ). Quantify the quality of model simulations using RMSE, Pattern and Pearson Correlation Coefficients (Taylor, 2001). Cluster Analysis for model classification (Wilks, 2011). Factor Analysis for dimension reduction (Rencher, 2002).

4. Observations Basic features of the Choco jet Results as an inversion of the easterly winds. Choco jet is centered at 5 N and 80 W. Reference figure The jet core is at 925hPa. This jet has an annual cycle with peaks during Oct-Nov and a minor activity during Feb-March (Poveda and Mesa, 2000). Fig. 1 Monthly climatology for 925hPa zonal wind annual cycle from ERA-Interim in m/s. Major jet activity

4. Observations Basic features of the Choco jet This low-level jet exhibits a latitudinal amplification and deepening in the lower troposphere. Fig. 2 Seasonal climatology for zonal wind from ERA-Interim in m/s. Vertical structure of the jet.. Jet core Latitudinal amplification and migration of the jet. Fig. 3 Seasonal climatology for zonal wind at 925hPa from ERA-Interim in m/s. Spatial distribution of the jet.

5. Cluster Analysis for Model Classification Use of RMSE and PCC of the basic features (annual cycle, vertical structure and spatial distribution) of the Chocó low-level jet as indicators of the quality of model representation. 3 Optimal clusters 3 Optimal clusters Fig. 4 Cluster analysis for model classification among Best, Worst and Intermediate models.

6. Results Representation of the basic features of 6.1 the Choco jet: Annual Cycle Reference figure Worst: bias in the latitudinal location of the jet (southern). No Jet activity between April-May. Major activity during August-October. Best and AMIP: major activity of the jet during August-October and southern location between October and December. Fig. 5 Annual cycle differences of zonal winds from ERA-Interim at 81 W and 925hPa for Worst, Best and AMIP groups in m/s.

Representation of the basic features 6.2 of the Choco jet: Vertical Strucure Worst: Shallower jet, mid-tropospheric winds. especially during SON. Stronger zonal Best: Good representation of the vertical depth during DJF and MAM, but shallower jet in SON. Slightly stronger zonal mid-tropospheric winds. AMIP: Northern jet during MAM. Stronger zonal mid-tropospheric winds particularly during MAM and JJA. WORST BEST AMIP hpa Reference figure Fig. 6 Vertical structure of the Choco Jet (zonal wind differences from ERA-Interim (m/s)) at 1000-700 hpa, 81 W for Worst (left), Best (middle) and AMIP (rigth) models during SON.

6.3 Representation of the basic features of the Choco jet: Spatial Distribution Change of winds direction due to topography Overestimation easterly winds of Underestimation of winds coming from the south Fig. 7 Seasonal climatology for horizontal wind (m/s) at 925 hpa represented by ERA-Interim, Worst, Best and AMIP models during SON.

6.3 Representation of the basic features of the Choco jet: Spatial Distribution Worst: Stronger easterly winds over Caribbean sea and northeastern Pacific. Absence of the change in direction of horizontal winds due to topographic barrier. Underestimation of the winds coming from Peru and Ecuador coasts. Best: Horizontal shift due to topography. Better winds coming from Peru and Ecuador. Better location of the latitudinal range where westerly winds get into the continent. AMIP: Better representation during DJF and MAM and worse representation in JJA and SON.

that explain 6.4 Mechanisms existence of the Choco jet Colom ColPac Niño 1-2 Fig. 8 Explicative gradient regions for temperature and sea level pressure for Choco jet. (Poveda and Mesa 2000) the Differences of temperature and sea level pressure among the regions. Topographic lifting. Interaction winds. with easterly Change of Coriolis sign. Latent heat MCSs. release in

Mechanisms that explain the 6.4 existence of the Choco jet Reference figure Better representation for Best and AMIP models. Worst representation in differences between Colom and Colpac regions. Annual cycle peaks one month in advance. ERA Fig. 10 Annual cycle of regional differences for temperature and pressure at 1000hPa.

6.4 Mechanisms that explain the existence of the Choco jet Best Worst Table. 1 RMSE and bias for topography in the region 10 S-12 N, 85-60 W. Best models present a good topography representation. Worst models present a misrepresentation of topography. Underestimation of topographic gradient. the The low-level circulation is sensitive to the height of the Andes (Saurral et al. 2014).

6.5 Consequences of the representation of the Choco jet Reference figure: Monthly correlations between Choco jet index and precipitation anomalies (Sierra et al. 2015). ERA Fig. 11 Choco index annual cycle and precipitation annual cycle for western Colombia (80-76 W, 2-9 N). Choco index is defined as the zonal wind average at 81 W, 925hPa between 5 S-7 N (Poveda and Mesa, 2000). A better representation of the Choco jet could be related with a better representation of precipitation annual cycle for western Colombia, especially during October-November-December.

7. Conclusions All models present the existence of the westerly winds at the eastern tropical Pacific during almost the entire year. Models present the maximum activity of the Choco jet 2-1 months earlier. This error comes from the atmospheric component of models. Worst models tend to keep the jet further south, which seems to be related with a misrepresentation of the inter-hemispherical temperature differences. Models in general tend to present a southward migration of the jet during SON and December. This error comes from the atmospheric component of models. Underestimation of the jet core velocities during most of the year.

7. Conclusions Misrepresentation temperature and differences. of the ocean-land pressure annual cycle Topography representation can lead to a better representation of the jet. A better representation of the jet can improve the representation of precipitation over certain regions of Colombia.

8. Acknowledgments This work was supported by Departamento Administrativo de Ciencia, Tecnología e Innovación de Colombia (Colciencias) Program no. 5509-543-31966. Based on: Juan P. Sierra, Jhoana Agudelo, Paola A. Arias and Sara C. Vieira. The western Colombia low-level jet and its simulation by CMIP5 models (In preparation). Corresponding author: pablo.sierra@udea.edu.co