diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py
index cc0cfc49..133a79e2 100644
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@@ -659,6 +659,22 @@ def ripley_synth(data_set='ripley_prnn_data'):
     ytest = test[:, 2:3]
     return data_details_return({'X': X, 'Y': y, 'Xtest': Xtest, 'Ytest': ytest, 'info': 'Synthetic data generated by Ripley for a two class classification problem.'}, data_set)
 
+def global_average_temperature(data_set='global_temperature', num_train=1000, refresh_data=False):
+    path = os.path.join(data_path, data_set)
+    if data_available(data_set) and not refresh_data:
+        print 'Using cached version of the data set, to use latest version set refresh_data to True'
+    else:
+        download_data(data_set)
+    data = np.loadtxt(os.path.join(data_path, data_set, 'GLBTS.long.data'))
+    print 'Most recent data observation from month ', data[-1, 1], ' in year ', data[-1, 0]
+    allX = data[data[:, 3]!=-99.99, 2:3]
+    allY = data[data[:, 3]!=-99.99, 3:4]
+    X = allX[:num_train, 0:1]
+    Xtest = allX[num_train:, 0:1]
+    Y = allY[:num_train, 0:1]
+    Ytest = allY[num_train:, 0:1]
+    return data_details_return({'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'info': "Mauna Loa data with " + str(num_train) + " values used as training points."}, data_set)
+
 def mauna_loa(data_set='mauna_loa', num_train=545, refresh_data=False):
     path = os.path.join(data_path, data_set)
     if data_available(data_set) and not refresh_data: