diff --git a/modules/Crossval/Crossval_anomalies.R b/modules/Crossval/Crossval_anomalies.R
index fe0a2e0576ca4694112b9b416f20bc8cd518e72c..280850ddb3de7e2f69e1f79019d1d122a1466249 100644
--- a/modules/Crossval/Crossval_anomalies.R
+++ b/modules/Crossval/Crossval_anomalies.R
@@ -81,14 +81,14 @@ Crossval_anomalies <- function(recipe, data) {
                               fun = function(x, prob_lims) {
                                 lapply(prob_lims, function(ps) {
                                   as.array(quantile(as.vector(x), ps,
-                                                    na.rm = na.rm))})},
+                                                    na.rm = TRUE))})},
                               prob_lims = list(unlist(categories)),
                               ncores = ncores)$output1
     lims_ano_obs_tr <- Apply(ano_obs_tr, target_dims = c('syear', 'ensemble'),
                              fun = function(x, prob_lims) {
                                lapply(prob_lims, function(ps) {
                                  as.array(quantile(as.vector(x), ps,
-                                                   na.rm = na.rm))})},
+                                                   na.rm = TRUE))})},
                              prob_lims = list(unlist(categories)),
                              ncores = ncores)$output1
     if (tolower(recipe$Analysis$Horizon) == 'subseasonal') {
@@ -176,7 +176,7 @@ Crossval_anomalies <- function(recipe, data) {
                        fun = function(x, prob_lims) {
                          lapply(prob_lims, function(ps) {
                                 as.array(quantile(as.vector(x), ps,
-                                                  na.rm = na.rm))})},
+                                                  na.rm = TRUE))})},
                        output_dims = lapply(categories, function(x) {'bin'}),
                        prob_lims = categories,
                        ncores = ncores)
@@ -190,7 +190,7 @@ Crossval_anomalies <- function(recipe, data) {
                   fun = function(x, prob_lims) {
                     lapply(prob_lims, function(ps) {
                              as.array(quantile(as.vector(x), ps,
-                                               na.rm = na.rm))})},
+                                               na.rm = TRUE))})},
                   output_dims = lapply(categories, function(x) {'bin'}),
                   prob_lims = categories,
                   ncores = ncores)
@@ -328,12 +328,13 @@ Crossval_anomalies <- function(recipe, data) {
     save_probabilities(recipe = recipe, probs = probs_obs,
                        data_cube = data$hcst, agg = agg,
                        type = "obs")
-    if (!is.null(probs_fcst)) {
+    if (length(probs_fcst) > 0) {
       save_probabilities(recipe = recipe, probs = probs_fcst,
                          data_cube = data$fcst, agg = agg,
                          type = "fcst")
     }
   }
+
   # Save ensemble mean for multimodel option:
   hcst_EM <- MeanDims(ano_hcst$data, 'ensemble', drop = T)
 #  save_metrics(recipe = recipe,
@@ -349,6 +350,8 @@ Crossval_anomalies <- function(recipe, data) {
 #                   Subset(fcst_EM, along = 'dat', indices = 1, drop = 'selected')),
 #                 data_cube = data$fcst, agg = agg,
 #                 module = "statistics")
+  } else {
+    fcst_EM <- NULL
   }
   return(list(hcst = ano_hcst, obs = ano_obs, fcst = data$fcst,
               hcst.full_val = data$hcst, obs.full_val = data$obs,